Best GPUs for Local LLM Inference (2026)

TL;DR

Top pick: NVIDIA RTX 5090 (~$1,999) -- 32GB GDDR7 with 1,792 GB/s bandwidth, runs 70B+ models at Q4.
Best value: NVIDIA RTX 3090 used (~$700-975) -- 24GB at $30-40/GB, best VRAM-per-dollar deal.
Best budget: NVIDIA RTX 5060 Ti (~$430) -- 16GB GDDR7, 51 tok/s on 8B models.

VRAM is the hard ceiling for LLM inference -- if the model does not fit, performance collapses 5-20x regardless of compute power. [src1, src4]

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. 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) is the new consumer champion, breaking the 24GB ceiling with 1,792 GB/s memory bandwidth -- 78% faster than the RTX 4090. The RTX 3090 remains the best value play at $700-975 used, offering 24GB and 936 GB/s bandwidth. For budget builders, the RTX 5060 Ti (16GB GDDR7, ~$430) delivers 51 tok/s on 8B models, outperforming the $1,200+ RTX 4080 SUPER on a per-dollar basis. On the AMD side, the RX 7900 XTX (24GB, ~$750-900) offers the best VRAM-per-dollar, and ROCm 7.2 (March 2026) finally achieved full parity with CUDA on Linux. [src4, src5, src6]

Best Overall: NVIDIA RTX 5090 (~$1,999) -- Check price

The undisputed consumer champion for LLM inference in 2026. 32GB of GDDR7 VRAM breaks the 24GB limit, allowing dense 32B models to run with 32k token context windows. 1,792 GB/s bandwidth sustains ~6,900-7,000 tok/s prompt processing on 8B models at 16k context. Runs 70B models at Q4 quantization with VRAM to spare. [src1, src4]

Best Value (Used Market): NVIDIA RTX 3090 (~$700-975) -- Check price

Six years after launch, 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 at $700-975 work out to ~$30-40 per GB of VRAM. Two of these ($1,400-1,950) give 48GB total VRAM for less than one RTX 5090. Downsides: 350W TDP, physically massive, used-market risk. [src5, src1]

Best Budget: NVIDIA RTX 5060 Ti (~$430) -- Check price

Top value pick for budget builders. 51 tok/s on 8B models at ~$430, outperforming the RTX 4060 Ti 16GB (34 tok/s). 16GB GDDR7 handles 7B models with long context or 20B quantized models. The RTX 5070 Ti at $812 gains only ~29% speed on 20B models. [src4]

Best for Large Models (70B+): NVIDIA RTX 5090 (~$1,999) -- Check price

The only single consumer card that can run 70B models at Q4 quantization with meaningful context lengths. 32GB VRAM leaves headroom for KV cache after loading a 70B Q4 model (~40GB). For 120B+ models, the RTX PRO 6000 (96GB) is needed. [src1, src2]

Best AMD Option: AMD RX 7900 XTX (~$750-900) -- Check price

Best AMD GPU for local LLM inference under $1,000. 24GB GDDR6 at ~$31-37/GB -- cheaper than any NVIDIA 24GB option. ROCm 7.2 (March 2026) achieves full Ollama/llama.cpp/vLLM parity with CUDA on Linux. Inference speed lags NVIDIA: 14-18 tok/s on Llama 3 70B Q4 vs ~55 tok/s on RTX 4090. Linux-only. [src6, src1]

Best Mid-Range: NVIDIA RTX 5080 (~$1,199) -- Check price

Performance monster for 16GB. Ideal for 34B models at high quantization. 960 GB/s bandwidth with 5th-gen Tensor Cores, delivering 40-55 tok/s on 8B models. Best for users wanting Blackwell speed without the $2,000 price tag. [src1]

Best Professional: NVIDIA RTX PRO 6000 (~$7,000-10,000) -- Check price

96GB VRAM on a single card. Run unquantized 32B models or 70B at Q8 without multi-GPU complexity. ~32 tok/s on Llama 3.3 70B Q4 and 7,500+ tok/s prompt processing on 8B models. Pays for itself in 1-3 years vs $200-500/month cloud API costs. [src5, src4]

Head-to-Head Comparisons

RTX 5090 vs RTX 4090

The RTX 5090 delivers 35-46% more tok/s, driven by 78% more memory bandwidth (1,792 vs 1,008 GB/s) and 8GB more VRAM. The 5090 runs 70B Q4 models comfortably where the 4090 barely fits them. The 4090 wins on cost-per-token for workloads that fit in 24GB. [src4, 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 ~1.9x faster in bandwidth (1,792 vs 936 GB/s) and has 8GB more VRAM, but costs 2-3x as much. The 3090 still runs 32B models at Q4 and hits 66-88 tok/s on 14B models. Two used 3090s ($1,400-1,950) provide 48GB total for less than one 5090. [src5, src1]

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

RTX 5060 Ti vs RTX 5070 Ti

Both have 16GB GDDR7, so they run the same models. The 5070 Ti is ~29% faster on 20B models (66 vs 43 tok/s) but costs nearly double ($812 vs $430). The 5060 Ti at 51 tok/s on 8B is fast enough for comfortable daily use. [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 generation and have the budget.

RTX 4090 vs RX 7900 XTX

Both have 24GB but the RTX 4090 is dramatically faster: ~55 tok/s on 8B vs ~14-18 tok/s for 70B Q4 on the 7900 XTX. The 7900 XTX costs ~$750-900 vs $1,800-2,200, but requires Linux with ROCm 7.2+. [src6, src1]

Pick RTX 4090 if: you want maximum 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 < $500

→ RTX 5060 Ti (~$430). Best performance-per-dollar in the 16GB tier. Handles 7B-20B models at Q4 with 51 tok/s on 8B. [src4]

If budget is $500-$1,000 and VRAM matters most

→ Used RTX 3090 (~$700-975) for 24GB at $30-40/GB, or RX 7900 XTX (~$750-900) if on Linux. [src5, src1]

If primary use is 7B-14B models for daily coding

→ Prioritize bandwidth over VRAM capacity. RTX 5060 Ti (~$430) or RTX 5070 Ti (~$812). 16GB is plenty. [src4]

If user needs 70B+ model support on a single card

→ RTX 5090 (~$1,999). Only consumer card with 32GB. Alternatively, dual RTX 3090s ($1,400-1,950) for 48GB. [src1, src2]

If user runs Linux and wants max VRAM per dollar

→ AMD RX 7900 XTX (~$750-900) at ~$31-37/GB. ROCm 7.2 achieves full CUDA parity for inference. [src6]

Default recommendation

→ Used RTX 3090 (~$700-975). 24GB handles the widest range of models, CUDA compatibility is bulletproof. [src5, src1]

• 32GB consumer VRAM barrier broken: RTX 5090 is the first consumer card to exceed 24GB (32GB GDDR7), enabling single-card 70B inference for the first time. [src1, src2]
• GDDR7 delivers transformative bandwidth: Blackwell cards deliver 50-78% more bandwidth than predecessors, directly translating to faster token generation since inference is bandwidth-bound. [src4]
• AMD ROCm reaches parity: ROCm 7.2 (March 2026) works out-of-the-box with Ollama, LM Studio, llama.cpp, and vLLM on Linux. AMD GPUs are finally viable for inference, though NVIDIA still leads on speed. [src6]
• Used RTX 3090 market stabilized: Prices settled at $700-975 after years of volatility. Still the most-recommended card for local AI. [src5]
• Quantization eliminates the quality gap: Q4_K_M reduces VRAM ~75% vs FP16 with minimal quality loss, making 24GB cards viable for 70B models. [src1, src7]
• RTX PRO 6000 enables single-card 120B+: 96GB eliminates multi-GPU complexity for the largest open-weight models. Pays for itself in 1-3 years vs cloud API costs. [src5]

• Prices are approximate US street prices as of May 2026. GPU prices fluctuate; the RTX 5090 has been above MSRP due to demand.
• Token/s benchmarks vary by model, quantization, context length, and software stack. Numbers cited are representative mid-range figures from multiple testing sources.
• Used GPU purchases carry risk -- no manufacturer warranty, potential mining wear, possible VRAM degradation.
• AMD ROCm support is Linux-only for reliable LLM inference. Windows AMD users should expect compatibility issues.
• Multi-GPU setups add complexity and do not scale linearly -- expect ~70-80% of theoretical combined performance.
• KV cache memory scales with context length and is often underestimated. A 70B Q4 model may load in 40GB but requires additional VRAM for conversation context.