---
# === IDENTITY ===
id: computing/components/gpus-for-llm-inference/2026
canonical_question: "What are the best GPUs for local LLM inference in 2026?"
aliases:
  - "best GPU for running AI models locally"
  - "GPU for ollama and llama.cpp"
  - "best GPU VRAM for large language models"
  - "RTX 5090 vs RTX 4090 for LLM inference"
  - "best budget GPU for local AI"
  - "how much VRAM do I need for LLMs"
  - "compare RTX 5090 vs RX 7900 XTX for AI"
  - "best GPU for running 70B models locally"
entity_type: product_comparison
domain: computing > components > gpus_for_llm_inference
region: global
jurisdiction: global
temporal_scope: 2025-2026

# === VERIFICATION ===
last_verified: 2026-07-16
confidence: 0.91
version: 1.0
first_published: 2026-05-09

# === TEMPORAL VALIDITY ===
temporal_validity:
  status: volatile
  last_breaking_change: "RTX 5090 (32GB GDDR7) launched Q1 2026 as the new consumer LLM king. RTX 5060 Ti 16GB launched Q2 2026 as the best budget option. AMD ROCm 7.2 (March 2026) achieved full Ollama/llama.cpp/vLLM parity with CUDA. Consumer GPU street prices climbed further through mid-2026 amid a GDDR memory shortage (RTX 5090 ~$4,330, RTX PRO 6000 ~$12,380, RX 7900 XTX ~$1,400 by July 2026)."
  next_review: 2026-08-15
  change_sensitivity: high

# === CONSTRAINTS ===
constraints:
  - "VRAM is a hard ceiling: if the model does not fit entirely in VRAM, performance drops 5-20x due to CPU offloading. No amount of compute power compensates."
  - "Token generation is memory-bandwidth-bound, not compute-bound. Higher bandwidth (GB/s) matters more than TFLOPS for inference speed."
  - "AMD GPUs require ROCm 7.2+ on Linux. Windows support for AMD LLM inference is limited. NVIDIA CUDA works on Windows and Linux with zero configuration."
  - "Quantization (Q4_K_M) reduces VRAM requirements ~75% vs FP16 with minimal quality loss for most use cases, making 24GB cards viable for 70B models."

# === SKIP CONDITIONS ===
skip_this_unit_if:
  - condition: "User needs GPUs for LLM training or fine-tuning, not inference"
    use_instead: "computing/components/gpus-for-ai-training/2026"
  - condition: "User wants cloud GPU rental rather than purchasing hardware"
    use_instead: "Search knowledgelib.io for cloud GPU rental providers — no dedicated unit yet"
  - condition: "User is on a Mac and wants to use Apple Silicon unified memory"
    use_instead: "computing/laptops/laptops-for-ai-ml-developers/2026"
  - condition: "User needs enterprise/datacenter GPUs (H100, H200, B200) for production serving"
    use_instead: "Search knowledgelib.io for enterprise datacenter GPUs — no dedicated unit yet"

# === AGENT HINTS ===
inputs_needed:
  - key: budget
    question: "What is your budget for the GPU?"
    type: choice
    options: ["Under $500", "$500-$1,000", "$1,000-$2,000", "$2,000+"]
  - key: model_size
    question: "What model sizes do you need to run?"
    type: choice
    options: ["7-8B (basic)", "13-32B (capable)", "70B+ (frontier)", "Multiple sizes"]
  - key: platform
    question: "What operating system will you use?"
    type: choice
    options: ["Windows", "Linux", "Either"]

# === DISTRIBUTION ===
canonical_source: "https://knowledgelib.io/computing/components/gpus-for-llm-inference/2026"
suggested_citation: "Source: knowledgelib.io — AI Knowledge Library (verified 2026-07-16)"

# === BUY LINKS ===
buy_links:
  - slug: "rtx-5090-gpus-for-llm-inference"
    product_name: "ASUS ROG Astral NVIDIA GeForce RTX 5090 32GB GDDR7 OC Edition Gaming Graphics Card"
    asin: "B0DS2WQZ2M"
    retailer: amazon_us
    destination_url: "https://www.amazon.com/dp/B0DS2WQZ2M?tag=knowledgelib-20"
  - slug: "rtx-4090-gpus-for-llm-inference"
    product_name: "PNY GeForce RTX 4090 24GB GDDR6X Verto Triple Fan Graphics Card"
    asin: "B0BHBTJ2X2"
    retailer: amazon_us
    destination_url: "https://www.amazon.com/dp/B0BHBTJ2X2?tag=knowledgelib-20"
  - slug: "rtx-3090-used-gpus-for-llm-inference"
    product_name: "Gigabyte NVIDIA GeForce RTX 3090 Turbo 24GB GDDR6X Graphics Card (Renewed)"
    asin: "B09Y2K9G3X"
    retailer: amazon_us
    destination_url: "https://www.amazon.com/dp/B09Y2K9G3X?tag=knowledgelib-20"
  - slug: "rtx-5080"
    product_name: "ASUS TUF Gaming GeForce RTX 5080 16GB GDDR7 OC Edition Graphics Card"
    asin: "B0DQSMMCSH"
    retailer: amazon_us
    destination_url: "https://www.amazon.com/dp/B0DQSMMCSH?tag=knowledgelib-20"
  - slug: "rtx-5070-ti"
    product_name: "ASUS TUF Gaming NVIDIA GeForce RTX 5070 Ti 16GB GDDR7 OC Edition Graphics Card"
    asin: "B0DS6WTXGP"
    retailer: amazon_us
    destination_url: "https://www.amazon.com/dp/B0DS6WTXGP?tag=knowledgelib-20"
  - slug: "rtx-5060-ti"
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    retailer: amazon_us
    destination_url: "https://www.amazon.com/dp/B0F7WB6LSH?tag=knowledgelib-20"
  - slug: "rx-7900-xtx-gpus-for-llm-inference"
    product_name: "ASRock AMD Radeon RX 7900 XTX Phantom Gaming 24GB OC GDDR6 Graphics Card"
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    retailer: amazon_us
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    asin: "B0FKD5GF8W"
    retailer: amazon_us
    destination_url: "https://www.amazon.com/dp/B0FKD5GF8W?tag=knowledgelib-20"

# === RELATED UNITS ===
related_kos:
  related_to:
    - id: "computing/laptops/laptops-for-ai-ml-developers/2026"
      label: "Best Laptops for AI/ML Developers (2026)"
  often_confused_with:
    - id: "computing/components/gpus-for-ai-training/2026"
      label: "Best GPUs for AI Training (training requires more VRAM and compute than inference)"
  depends_on: []
  solves: []

# === SOURCES ===
sources:
  - id: src1
    title: "LLM GPU Buyer's Guide (April 2026): Best VRAM per Dollar Tier List"
    author: CoreLab
    url: https://corelab.tech/llmgpu/
    type: product_testing
    published: 2026-04-15
    reliability: high
  - id: src2
    title: "Best GPU for LLM Inference and Training -- 2026 [Updated]"
    author: BIZON
    url: https://bizon-tech.com/blog/best-gpu-llm-training-inference
    type: product_testing
    published: 2026-04-20
    reliability: high
  - id: src3
    title: "7 Best GPU for LLM in 2026 (Including Local LLM Setups)"
    author: Fluence
    url: https://www.fluence.network/blog/best-gpu-for-llm/
    type: product_testing
    published: 2026-04-10
    reliability: moderate_high
  - id: src4
    title: "The Definitive GPU Ranking for LLMs: Token Generation & Prompt Processing Performance"
    author: Hardware Corner
    url: https://www.hardware-corner.net/gpu-ranking-local-llm/
    type: product_testing
    published: 2025-12-09
    reliability: high
  - id: src5
    title: "Best GPUs for Running Local LLMs: Buyer's Guide 2026"
    author: Houtini
    url: https://houtini.com/best-gpus-for-running-local-llms/
    type: product_testing
    published: 2026-04-25
    reliability: high
  - id: src6
    title: "Best AMD GPU for Local LLM Inference 2026 -- Buyer Guide"
    author: Compute Market
    url: https://www.compute-market.com/blog/best-amd-gpu-local-llm-inference-2026
    type: product_testing
    published: 2026-05-01
    reliability: moderate_high
  - id: src7
    title: "GPU Requirements Cheat Sheet 2026: Every Major AI Model"
    author: Spheron
    url: https://www.spheron.network/blog/gpu-requirements-cheat-sheet-2026/
    type: industry_report
    published: 2026-03-15
    reliability: moderate_high
  - id: src8
    title: "RTX 5090 LLM Benchmark Results: 10K Tokens/sec Prompt Processing, 139K Context"
    author: Hardware Corner
    url: https://www.hardware-corner.net/rtx-5090-llm-benchmarks/
    type: product_testing
    published: 2026-05-20
    reliability: high
  - id: src9
    title: "Best GPU for Local LLMs & Inference (2026): Top Picks by Budget"
    author: Decodes Future
    url: https://www.decodesfuture.com/articles/best-gpu-for-local-llms-2026-guide
    type: product_testing
    published: 2026-05-15
    reliability: moderate_high
---

# Best GPUs for Local LLM Inference (2026)

## What are the best GPUs for local LLM inference in 2026?

## TL;DR

**Top pick: NVIDIA RTX 5090 (street ~$4,330; $1,999 MSRP) -- 32GB GDDR7 with 1,792 GB/s bandwidth, runs 70B+ models at Q4 with 45-48 tok/s on 8B and 10,000+ tok/s prompt processing.**
**Best value: NVIDIA RTX 3090 used (~$800-1,000 on eBay; ~$1,430 renewed on Amazon) -- 24GB GDDR6X, runs 32B models comfortably and 70B at Q4, still the best VRAM-per-dollar deal.**
**Best budget: NVIDIA RTX 5060 Ti 16GB (~$560) -- 16GB GDDR7, 51 tok/s on 8B models, the sweet spot for 7-20B parameter models.**
VRAM is the hard ceiling for LLM inference in 2026 -- if the model does not fit, performance collapses 5-20x regardless of compute power. Note: consumer GPU street prices climbed further above MSRP through mid-2026 amid a GDDR memory shortage.
[src1, src8]

## Summary

The GPU market for local LLM inference in 2026 revolves around one spec above all others: VRAM capacity. Token generation is memory-bandwidth-bound -- the GPU spends most of its time loading model weights from VRAM, not computing. If a model does not fit entirely in VRAM, performance drops 5-20x due to CPU offloading, making the card effectively unusable for that model size. The rule of thumb is ~2GB of VRAM per billion parameters at FP16, or ~0.5GB per billion at Q4_K_M quantization. [src1, src4]

The **NVIDIA RTX 5090** (32GB GDDR7, $1,999 MSRP) is the new consumer champion, breaking past the long-standing 24GB ceiling with 1,792 GB/s memory bandwidth -- 78% faster than the RTX 4090. It handles 70B+ models at Q4 quantization and sustains over 10,000 tokens/sec prompt processing on 8B models (45-48 tok/s generation). Persistent demand plus a GDDR memory shortage have pushed street prices to ~$4,330, well above MSRP. The **RTX 3090** remains the best value play at ~$800-1,000 used on eBay (~$1,430 for a renewed unit on Amazon), offering 24GB GDDR6X and 936 GB/s bandwidth -- enough for 32B models at Q4 with room to spare. For budget builders, the **RTX 5060 Ti 16GB** (~$560) delivers 51 tok/s on 8B models, outperforming the $1,200+ RTX 4080 SUPER on a per-dollar basis. [src8, src5]

On the AMD side, the **RX 7900 XTX** (24GB) has climbed to ~$1,400 as RDNA 3 stock thins out (~$58/GB -- the used RTX 3090 still undercuts it on VRAM-per-dollar), but ROCm 7.2 (March 2026) finally achieved full parity with CUDA across Ollama, LM Studio, llama.cpp, and vLLM -- albeit only on Linux. For professionals who need to run unquantized 70B models or 120B+ parameter models, the **RTX PRO 6000** (96GB, ~$12,380) is the only single-card option. [src1, src6]

## Top 8 GPUs Compared

| Model | Price | VRAM | Bandwidth | Tok/s (8B Q4) | TDP | Best For | Buy |
|---|---|---|---|---|---|---|---|
| NVIDIA RTX 5090 | ~$4,330 street ($1,999 MSRP) | 32GB GDDR7 | 1,792 GB/s | 45-48 | 575W | Best overall / 70B+ models | [Check price](https://knowledgelib.io/go/rtx-5090-gpus-for-llm-inference) |
| NVIDIA RTX 4090 (EOL) | ~$3,495 (resellers) | 24GB GDDR6X | 1,008 GB/s | ~42 | 450W | Proven workhorse (discontinued) | [Check price](https://knowledgelib.io/go/rtx-4090-gpus-for-llm-inference) |
| NVIDIA RTX 3090 (used) | ~$1,430 renewed (~$800-1,000 used) | 24GB GDDR6X | 936 GB/s | ~38 | 350W | Best VRAM/dollar | [Check price](https://knowledgelib.io/go/rtx-3090-used-gpus-for-llm-inference) |
| NVIDIA RTX 5080 | ~$1,585 ($999 MSRP) | 16GB GDDR7 | 960 GB/s | ~50 | 360W | Fast 16GB option | [Check price](https://knowledgelib.io/go/rtx-5080) |
| NVIDIA RTX 5070 Ti | ~$1,074 | 16GB GDDR7 | 896 GB/s | ~66 (14B) | 300W | Mid-range performer | [Check price](https://knowledgelib.io/go/rtx-5070-ti) |
| NVIDIA RTX 5060 Ti | ~$560 | 16GB GDDR7 | 504 GB/s | ~51 | 180W | Best budget | [Check price](https://knowledgelib.io/go/rtx-5060-ti) |
| AMD RX 7900 XTX | ~$1,400 | 24GB GDDR6 | 960 GB/s | ~14-18 (70B Q4) | 355W | Best AMD 24GB | [Check price](https://knowledgelib.io/go/rx-7900-xtx-gpus-for-llm-inference) |
| NVIDIA RTX PRO 6000 | ~$12,380 | 96GB GDDR7 | 1,280 GB/s | ~32 (70B Q4) | 600W | Professional / 120B+ | [Check price](https://knowledgelib.io/go/rtx-pro-6000) |

## Best for Each Use Case

### Best Overall: NVIDIA RTX 5090 (~$4,330) -- [Check price](https://knowledgelib.io/go/rtx-5090-gpus-for-llm-inference)
The RTX 5090 is the undisputed consumer champion for LLM inference in 2026. Its 32GB of GDDR7 VRAM breaks past the long-standing 24GB limit, allowing dense 32B models to run with 32k token context windows. With 1,792 GB/s memory bandwidth (78% faster than the RTX 4090), it sustains over 10,000 tokens/sec prompt processing on 8B models (139k context demonstrated) and 45-48 tok/s generation on 8B at Q4. It runs 70B models at Q4 quantization with VRAM to spare. MSRP is $1,999 but persistent demand plus a GDDR memory shortage have pushed street prices to ~$4,330 through mid-2026 -- budget for the higher figure. For users who demand peak performance and maximum future-proofing, this is the card. [src1, src8]

### Best Value (Used Market): NVIDIA RTX 3090 (~$800-1,000) -- [Check price](https://knowledgelib.io/go/rtx-3090-used-gpus-for-llm-inference)
Six years after launch, the RTX 3090 is still the best deal in local AI hardware. 24GB of VRAM with 936 GB/s bandwidth runs 32B parameter models at Q4 with room to spare, hitting 66-88 tok/s on 14B models. Used prices sit at ~$800-1,000 on eBay as the broader GPU market tightened, working out to ~$35-42 per gigabyte of VRAM -- still unbeatable; renewed units on Amazon run higher at ~$1,430. Two used cards (~$1,600-2,000 total) give 48GB of VRAM -- enough for 70B models at Q4. Downsides: 350W TDP, physically massive (triple-slot), and buying used carries risk. [src5, src1]

### Best Budget: NVIDIA RTX 5060 Ti 16GB (~$560) -- [Check price](https://knowledgelib.io/go/rtx-5060-ti)
The top value pick for budget builders. At ~$560, it delivers 51 tok/s on 8B models -- well above the RTX 4060 Ti 16GB's 34 tok/s at a similar cost. Its 16GB GDDR7 VRAM handles 7B models with long context or 20B quantized models comfortably. The RTX 5070 Ti at nearly double the cost (~$1,070) gains only ~29% more speed on 20B models, making the 5060 Ti the clear sweet spot for users who need capable 16GB inference without breaking the bank. Confirm you buy the 16GB variant -- the 8GB 5060 Ti is too cramped for most modern models. [src4, src9]

### Best for Large Models (70B+): NVIDIA RTX 5090 (~$4,330) -- [Check price](https://knowledgelib.io/go/rtx-5090-gpus-for-llm-inference)
The only single consumer card that can run 70B models at Q4 quantization with meaningful context lengths. Its 32GB VRAM leaves ~35% headroom for KV cache after loading a 70B Q4 model (~40GB), enabling 8-16k context. For 70B at higher quantization or 120B+ models, you need dual cards or the RTX PRO 6000. [src1, src2]

### Best AMD Option: AMD RX 7900 XTX (~$1,400) -- [Check price](https://knowledgelib.io/go/rx-7900-xtx-gpus-for-llm-inference)
The best AMD GPU for local LLM inference, now ~$1,400 as RDNA 3 stock thins out. 24GB GDDR6 with 960 GB/s bandwidth at ~$58 per GB of VRAM -- pricier than a used RTX 3090 per gigabyte, but the cheapest new 24GB card. ROCm 7.2 (March 2026) is the first AMD software release that achieves full Ollama, LM Studio, llama.cpp, and vLLM parity with CUDA out of the box. However, AMD inference speed lags behind NVIDIA: expect 14-18 tok/s on Llama 3 70B Q4, compared to ~42 tok/s on the RTX 4090. Linux-only for reliable LLM support. [src6, src1]

### Best Mid-Range: NVIDIA RTX 5080 (~$1,585) -- [Check price](https://knowledgelib.io/go/rtx-5080)
A performance monster for 16GB. Ideal for 14B-27B models at Q4 or 34B at high quantization. With 960 GB/s bandwidth and 5th-gen Tensor Cores, it massively outperforms the RTX 4080 and delivers 40-55 tok/s on 8B models (~132 tok/s on smaller models per newer benchmarks). MSRP is $999 but street prices have climbed to ~$1,585. Best for users who want Blackwell-generation speed without the RTX 5090's price tag, but can live with 16GB VRAM. [src1, src9]

### Best Professional: NVIDIA RTX PRO 6000 (~$12,380) -- [Check price](https://knowledgelib.io/go/rtx-pro-6000)
The nuclear option: 96GB of VRAM on a single card. Run unquantized 32B models, or 70B at Q8, without any multi-GPU complexity. Generates ~32 tok/s on Llama 3.3 70B at Q4 and over 7,500 tok/s prompt processing on 8B models. At ~$12,380, it is strictly for professionals, but compared to cloud API costs of $200-500/month, the card pays for itself in 1-3 years. [src5, src4]

## Head-to-Head Comparisons

### RTX 5090 vs RTX 4090
The RTX 5090 delivers 35-46% more tok/s than the RTX 4090, driven primarily by the 78% memory bandwidth jump (1,792 vs 1,008 GB/s) and 8GB more VRAM. The 5090 runs 70B Q4 models comfortably where the 4090 barely fits them. At prompt processing, the 5090 sustains 10,000+ tok/s vs ~4,200-4,800 for the 4090 on 8B models. The RTX 4090 is now end-of-life and only available from resellers at inflated prices, so the 5090 is the clear choice for new builds and the obvious upgrade for anyone running 32B+ models. [src8, src2]

**Pick RTX 5090 if:** you run 32B-70B models regularly or need maximum throughput.
**Pick RTX 4090 if:** your models fit in 24GB and you want proven, cheaper hardware.

### RTX 5090 vs RTX 3090 (Used)
The RTX 5090 is roughly 1.9x faster in bandwidth (1,792 vs 936 GB/s) and has 8GB more VRAM, but at street prices it costs 3-4x as much as a used RTX 3090. The 3090 still runs 32B models at Q4 with room to spare and hits 66-88 tok/s on 14B models. For users on a budget who do not need 70B+ model support, the 3090 remains the smarter buy. Two used 3090s (~$1,600-2,000) provide 48GB total VRAM for far less than one 5090 at current street prices. [src5, src1]

**Pick RTX 5090 if:** you want single-card 70B support and maximum speed.
**Pick RTX 3090 if:** budget matters more than peak speed and you can tolerate 350W power draw.

### RTX 5060 Ti vs RTX 5070 Ti
Both have 16GB GDDR7 VRAM, so they run the same models. The 5070 Ti is ~29% faster on 20B models (66 tok/s vs 43 tok/s), but costs nearly double (~$1,074 vs ~$560). The 5060 Ti delivers 51 tok/s on 8B models -- fast enough for comfortable daily use. The 5070 Ti's speed advantage is real but not transformative given the VRAM ceiling is identical. [src4]

**Pick RTX 5060 Ti if:** you want the best performance-per-dollar at 16GB.
**Pick RTX 5070 Ti if:** you need noticeably faster 14-20B model generation and have the budget.

### RTX 4090 vs RX 7900 XTX
Both have 24GB VRAM, but the RTX 4090 is dramatically faster for LLM inference: ~42 tok/s on 8B models vs ~14-18 tok/s on the 7900 XTX for 70B Q4 workloads. The 4090 is now effectively end-of-life and sells only through resellers at ~$3,495, so the RX 7900 XTX (~$1,400) is the cheaper path to new 24GB VRAM. However, the 7900 XTX requires Linux with ROCm 7.2+ for reliable LLM support -- Windows AMD support remains poor. [src6, src1]

**Pick RTX 4090 if:** you can find one near MSRP and want maximum inference speed, Windows support, and proven CUDA compatibility.
**Pick RX 7900 XTX if:** you run Linux, prioritize VRAM-per-dollar, and can tolerate slower inference.

## Decision Logic

### If budget < $600
--> **RTX 5060 Ti 16GB** (~$560). Best performance-per-dollar in the 16GB tier. Handles 7B-20B models at Q4 with 51 tok/s on 8B. The best sub-$600 card for daily LLM use -- always buy the 16GB variant, not the 8GB. [src4, src9]

### If budget is $600-$1,100 and VRAM matters most
--> **Used RTX 3090** (~$800-1,000 on eBay). 24GB of VRAM at ~$35-42/GB -- unbeatable for running 32B models and fitting 70B at Q4. Accept the power draw (350W) and used-market risk. A renewed 3090 on Amazon runs ~$1,430. [src5, src1]

### If primary use is 7B-14B models for daily coding assistance
--> Prioritize bandwidth over VRAM capacity. **RTX 5060 Ti 16GB** (~$560) or **RTX 5070 Ti** (~$1,074) -- 16GB is plenty for these model sizes, and their GDDR7 bandwidth delivers snappy generation. [src4]

### If user needs 70B+ model support on a single card
--> **RTX 5090** (~$4,330 street). Only consumer card with 32GB VRAM. Alternatively, dual used RTX 3090s (~$1,600-2,000) provide 48GB across two cards, but multi-GPU inference adds complexity. [src1, src2]

### If user runs Linux and wants a new 24GB AMD card
--> **AMD RX 7900 XTX** (~$1,400) at ~$58/GB. ROCm 7.2 achieves full parity with CUDA for inference. For pure VRAM-per-dollar a used RTX 3090 (~$900, CUDA) still wins, but the 7900 XTX is the cheapest new 24GB option. [src6]

### Default recommendation
--> **Used RTX 3090** (~$800-1,000 on eBay) for most users. 24GB VRAM handles the widest range of models, used prices remain accessible, and CUDA compatibility is bulletproof. If buying new, **RTX 5060 Ti 16GB** (~$560) for budget users or **RTX 5090** (~$4,330 street; $1,999 MSRP) for no-compromise performance. [src5, src1]

## VRAM Requirements by Model Size (Q4_K_M Quantization)

| Model Size | VRAM Needed (Q4_K_M) | VRAM at FP16 | Example Models | Minimum GPU |
|---|---|---|---|---|
| 7-8B | ~5-6 GB | ~14-16 GB | Llama 3.1 8B, Qwen 3 8B, Mistral 7B | RTX 5060 Ti (16GB) |
| 13-14B | ~8-10 GB | ~26-28 GB | CodeLlama 13B, Qwen 2.5 14B | RTX 5060 Ti (16GB) |
| 30-34B | ~18-22 GB | ~60-68 GB | Qwen 30B, DeepSeek-Coder-V2 | RTX 3090/4090 (24GB) |
| 70B | ~38-42 GB | ~140 GB | Llama 3.1 70B, Qwen 72B | RTX 5090 (32GB) or dual 24GB |
| 120B+ | ~65-80 GB | ~240+ GB | Llama 4 405B (quantized) | RTX PRO 6000 (96GB) |

**Critical note:** KV cache grows with context length. An 8B model's KV cache climbs from ~0.3GB at 2k context to ~5GB at 32k and over 20GB at 128k context. Factor this into your VRAM budget. [src6, src7]

## Key Market Trends (2026)

- **32GB consumer VRAM barrier broken**: The RTX 5090 is the first consumer card to exceed 24GB (32GB GDDR7), ending the 24GB ceiling that stood since the RTX 3090 in 2020. This enables single-card 70B inference for the first time. [src1, src2]
- **GDDR7 delivers transformative bandwidth**: Blackwell-generation cards (5060 Ti through 5090) use GDDR7, delivering 50-78% more memory bandwidth than their predecessors. Since inference is bandwidth-bound, this directly translates to faster token generation. [src4]
- **AMD ROCm reaches parity**: ROCm 7.2 (March 2026) is the first release that works out-of-the-box with Ollama, LM Studio, llama.cpp, and vLLM on Linux. AMD GPUs are finally a viable alternative for inference, though NVIDIA still leads on raw speed. [src6]
- **Used RTX 3090 prices crept up**: After years of volatility, used 3090 prices have firmed to ~$800-1,000 as the broader GPU market tightened. The 3090 remains the most-recommended card for local AI across Reddit, YouTube, and review sites. [src5]
- **Consumer GPU street prices surged above MSRP**: Through mid-2026, sustained AI/gaming demand plus a GDDR memory shortage pushed RTX 5090 listings to ~$4,330 (MSRP $1,999), RTX 5080 to ~$1,585 (MSRP $999), and RTX PRO 6000 to ~$12,380. The RTX 4090 has reached end-of-life and sells only through resellers at ~$3,495. Budget for street prices, not MSRP. [src9, src8]
- **SUPER refresh rumored with 24GB VRAM**: Leaks point to RTX 5080 SUPER (24GB, ~1,024 GB/s, ~$999 MSRP) and RTX 5070 Ti SUPER (24GB, ~896 GB/s, ~$749 MSRP) refreshes that would finally bring 24GB to the mid-range at non-SUPER prices. Unconfirmed by NVIDIA as of June 2026 -- buyers wanting 24GB on a new card may want to wait. [src9]
- **Quantization eliminates the quality gap**: Q4_K_M quantization reduces VRAM ~75% vs FP16 with minimal quality loss for most use cases. This makes 24GB cards viable for 70B models and 16GB cards practical for 20B+. [src1, src7]
- **RTX PRO 6000 enables single-card 120B+**: At 96GB VRAM, the RTX PRO 6000 (~$12,380) eliminates multi-GPU complexity for running the largest open-weight models. Compared to cloud API costs of $200-500/month, it pays for itself in 1-3 years. [src5]

## Important Caveats

- Prices are approximate US street prices as of July 2026. GPU prices fluctuate significantly; the RTX 5090 has traded at ~$4,330 (well above its $1,999 MSRP) since launch due to demand and a GDDR memory shortage, and the RTX 5080 around $1,585. Always check the live /go links for the current price.
- Token/s benchmarks vary by model, quantization level, context length, and software stack (Ollama vs llama.cpp vs vLLM). Numbers cited are representative mid-range figures from multiple testing sources.
- Used GPU purchases (RTX 3090) carry inherent risk -- no manufacturer warranty, potential mining wear, and possible VRAM degradation. Buy from reputable sellers with return policies.
- AMD ROCm support is Linux-only for reliable LLM inference. Windows AMD users should expect compatibility issues with Ollama and other LLM tools.
- Multi-GPU setups (e.g., dual RTX 3090s for 48GB) work with llama.cpp and Ollama but add complexity and do not scale linearly -- expect ~70-80% of theoretical combined performance.
- KV cache memory usage scales with context length and is often underestimated. A 70B Q4 model may load in 40GB but requires additional VRAM for the conversation context.

## Related Units

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