Best GPUs for LLM Inference in 2026
Updated August 26, 2026. All prices are launch MSRPs from our GPU database; we do not track street prices. Token speeds are third-party benchmark results, labeled with source and estimate status.
The best GPU for LLM inference depends on the largest model you want to run: VRAM capacity decides which models fit at all, and memory bandwidth decides how fast they generate tokens. For most people running local LLMs in 2026, the NVIDIA GeForce RTX 5090 is the best overall card because its 32 GB of GDDR7 is the only consumer VRAM pool that holds a quantized 70B model. The used GeForce RTX 3090 remains the best value at 24 GB, and the Intel Arc B580 is the cheapest new card that runs 7B–8B models well. This guide maps every current GPU into tiers by VRAM, gives minimum and recommended configurations for each popular model size, and breaks picks down by power and price bracket.
On this page
Short on time? Entry: Intel Arc B580 ($249, 12 GB) · Value: used RTX 3090 (24 GB) · Mainstream new: RTX 5070 Ti ($749, 16 GB) · Best overall: RTX 5090 ($1,999, 32 GB) · Multi-70B / long context: RTX PRO 6000 Blackwell (96 GB).
Not sure which card fits your model? Open the Model-to-GPU finder: pick a model, quantization, and context length — it computes the exact VRAM requirement and ranks every tracked GPU that runs it.
What are the best GPUs for LLM inference, ranked by budget?
Our ranked short answer, one pick per budget tier. Full reasoning for each pick is in the tier breakdown below.
- Under $300 — Intel Arc B580 ($249): the only new 12 GB card near this price; runs 7B–8B models at Q4 via Vulkan (41 tok/s recorded on Llama 3.1 8B).
- $300–500 — Used RTX 3090 or new RTX 5060 Ti 16GB ($429): the 3090 is the cheapest path to 24 GB and the 32B-class models that come with it; the 5060 Ti is the cheapest new 16 GB card if you want a warranty.
- $700–800 — GeForce RTX 5070 Ti ($749): best bandwidth-per-dollar of the new 16 GB cards at 896 GB/s; handles 14B at Q4–Q5 and fast 8B sessions.
- $900–1,100 — GeForce RTX 5080 ($999): fastest 16 GB card (132 tok/s recorded); choose the Radeon RX 7900 XTX ($999) instead only if 24 GB matters more to you than CUDA compatibility.
- $1,500–2,000 — GeForce RTX 5090 ($1,999): best overall — 32 GB fits quantized 70B, 1,792 GB/s makes everything below it fast (215 tok/s on 8B).
- No ceiling — RTX PRO 6000 Blackwell (96 GB): comfortable 70B+ with long context on one card; the data-center route is H100/A100 systems.
How do the best GPUs for LLM inference compare?
The table below lists every current consumer, workstation, and data-center GPU we recommend for local LLM inference, with datasheet specifications from our database and measured Llama 3.1 8B Q4_K_M token-generation speeds where we have them. To put any two of these cards side by side, use the GPU comparison tool.
| GPU | VRAM | Memory type | Bandwidth | TDP | MSRP | Llama-3.1-8B Q4_K_M (tok/s) |
|---|---|---|---|---|---|---|
| GeForce RTX 5090 | 32 GB | GDDR7 | 1,792 GB/s | 575 W | $1,999 | 215 |
| GeForce RTX 5080 | 16 GB | GDDR7 | 960 GB/s | 360 W | $999 | 132 |
| GeForce RTX 4090 | 24 GB | GDDR6X | 1,008 GB/s | 450 W | $1,599 | 125 |
| GeForce RTX 4080 SUPER | 16 GB | GDDR6X | 736 GB/s | 320 W | $999 | 102 |
| GeForce RTX 3090 (used) | 24 GB | GDDR6X | 936 GB/s | 350 W | $1,499 (launch) | 85 |
| Radeon RX 7900 XTX | 24 GB | GDDR6 | 960 GB/s | 355 W | $999 | 76 |
| GeForce RTX 5070 Ti | 16 GB | GDDR7 | 896 GB/s | 300 W | $749 | n/a |
| GeForce RTX 5060 Ti 16GB | 16 GB | GDDR7 | 448 GB/s | 180 W | $429 | n/a |
| GeForce RTX 4060 Ti 16GB | 16 GB | GDDR6 | 288 GB/s | 160 W | $499 | n/a |
| Arc B580 | 12 GB | GDDR6 | 456 GB/s | 190 W | $249 | 41 |
| RTX PRO 6000 Blackwell | 96 GB | GDDR7 ECC | 1,792 GB/s | 600 W | n/a (workstation) | n/a |
| H100 PCIe 80GB | 80 GB | HBM2e | 2,039 GB/s | 350 W | n/a (data center) | n/a |
Benchmark attribution: the 215 tok/s RTX 5090 figure is our llama.cpp benchmark record (single user, batch 1, 4k context, mean of 3 runs); the RTX 5080, RTX 4090, RTX 4080 SUPER, RTX 3090, RX 7900 XTX, and Arc B580 figures are llama.cpp results recorded in our benchmark database from llama-bench runs on CUDA, ROCm, and Vulkan respectively. Where a card has no recorded 8B result we mark it n/a rather than estimate.
Which GPU tier fits your model size?
Pick the tier that matches the largest model class you run day to day. Within a tier, prefer more bandwidth at the same VRAM — once a model fits, bandwidth is the main speed limiter.
A 12 GB card holds every 7B–8B model at Q4_K_M with 4k context, plus 13B-class models at tighter quantizations. The Arc B580 pairs its 12 GB with 456 GB/s of bandwidth at a $249 MSRP — the least expensive new card in our database that can do this. It runs llama.cpp through the Vulkan backend, so expect some tooling configuration compared with CUDA. The GeForce RTX 3060 12GB ($329 launch MSRP, now mostly eol stock) is the NVIDIA alternative with the widest software compatibility and a measured 38 tok/s on Llama 3.1 8B Q4_K_M.
Sixteen gigabytes is the sweet spot for 8B–14B models at higher quantizations (Q6/Q8), long-context 8B sessions, and image-generation models like FLUX.1 alongside your LLM. The RTX 5070 Ti combines 16 GB of GDDR7 with 896 GB/s of bandwidth at a $749 MSRP. The RTX 5060 Ti 16GB ($429) and RTX 4060 Ti 16GB ($499) carry the same capacity for less money but with much lower bandwidth (448 and 288 GB/s), which shows directly in tokens per second. The RTX 5080 ($999, 960 GB/s) is the fastest 16 GB card and measured 132 tok/s on Llama 3.1 8B Q4_K_M in our records. Head-to-head details: RTX 5070 Ti vs RTX 4080 SUPER.
Twenty-four gigabytes is the minimum comfortable pool for today's most capable local models: Gemma 3 27B, Mistral Small 3.2 24B, DeepSeek R1-Distill 32B, and Qwen 3 32B all fit at Q4_K_M with 4k context. The used RTX 3090 delivers that capacity with 936 GB/s of bandwidth and a measured 85 tok/s on Llama 3.1 8B Q4_K_M — VRAM per dollar nothing else in this guide matches. Its costs are the risks of any used purchase: no warranty, unknown history, and a 350 W draw. The RTX 4090 ($1,599 MSRP) adds 72 GB/s of bandwidth and a warranty, measuring 125 tok/s on the same test. AMD's Radeon RX 7900 XTX ($999, 24 GB, 960 GB/s) is the value alternative if you accept ROCm/Vulkan setup; it measured 76 tok/s on the same benchmark. We compared them directly in RX 7900 XTX vs RTX 4090.
Check used RTX 3090 prices → Check RTX 4090 prices → Check RX 7900 XTX prices →
The RTX 5090's 32 GB of GDDR7 is the largest consumer VRAM pool, and its 1,792 GB/s is the highest bandwidth of any single-GPU card in this guide. It measured 215 tok/s on Llama 3.1 8B Q4_K_M in our records — roughly 2.5× the used RTX 3090 — and our Tom's Hardware-sourced record has it running Llama 3 70B at Q4 at about 42 tok/s, flagged as an editorial estimate because the model is a tight fit even at 32 GB. Buy it when 70B-class quality matters daily or when you want one card that never forces a quantization compromise below 70B — our RTX 5090 vs RTX 4090 breakdown covers whether the premium buys you enough for LLM work. At 575 W it also demands a real PSU plan (see brackets below).
A 70B model at Q4 needs roughly 40 GB before context, which is why our model-fit database lists 48 GB as the minimum for Llama 3.x 70B and 80 GB+ as the recommendation for full context at Q5/Q6. The RTX 6000 Ada (48 GB, $6,800 MSRP) fits that minimum on one card; the RTX PRO 6000 Blackwell doubles it with 96 GB of GDDR7 ECC at the same 1,792 GB/s as the RTX 5090, holding 70B at high quantizations with room for long context. These are workstation cards with pro drivers, ECC memory, and blower cooling for multi-card chassis — priced accordingly.
Above 96 GB you are in data-center territory: the H100 80GB (2,039 GB/s, 350 W PCIe) and A100 80GB serve 70B-class models to many concurrent users, and MLPerf-derived records in our database put H100 Llama 3 70B Q4 generation near 90 tok/s per card (flagged as an estimate). AMD's Instinct MI300X answers with 192 GB of HBM3 at 5,300 GB/s for the largest models. Apple Silicon takes a different path: the M3 Ultra Mac Studio scales unified memory to 512 GB, so it holds models no discrete GPU can — at 819 GB/s of bandwidth, so tokens per second stay far below NVIDIA cards on models that fit both. Our Mac Studio vs PC for AI guide covers that trade-off in depth.
What are the minimum vs recommended configs for popular models?
The table below comes from our model-fit database, which computes VRAM requirements from parameter counts and quantization sizes (Q4_K_M ≈ 4.8 bits-per-weight plus KV-cache and activation overhead). Every row links to the full model page with per-GPU fit lists.
| Model | Params | Minimum config | Recommended config |
|---|---|---|---|
| Llama 3.1 8B | 8B | 8 GB GPU at Q4_K_M, 4k ctx | 12 GB GPU (RTX 3060 12GB class), Q4_K_M–Q6_K |
| Gemma 3 27B | 27B | 24 GB GPU (RTX 3090/4090) at Q4_K_M, 4k ctx | 32 GB+ (RTX PRO 6000 Blackwell or 2× 24 GB) at Q5_K_M+ |
| Mistral Small 3.2 | 24B | 20 GB GPU at Q4_K_M, 4k ctx | 24 GB GPU (RTX 3090/4090) at Q6_K–Q8_0 |
| DeepSeek R1-Distill | 32B | 24 GB GPU (RTX 3090/4090) at Q4_K_M, 4k ctx | 32 GB+ (RTX PRO 6000 Blackwell or 2× 24 GB) at Q4_K_M–Q6_K |
| Qwen 3 | 32B | 24 GB GPU (RTX 3090/4090) at Q4_K_M, 4k ctx | 32 GB+ (RTX PRO 6000 Blackwell or 2× 24 GB) at Q6_K–Q8_0 for 32k ctx |
| Llama 3.1 / 3.3 70B | 70B | 48 GB GPU (RTX A6000 / RTX 6000 Ada / L40S) at Q4_K_M, 4k ctx | 80 GB (H100/A100) at Q5_K_M–Q6_K for full context, or 2× RTX PRO 6000 Blackwell 96 GB |
| DeepSeek V4-Flash | 284B MoE (13B active) | 4× 80 GB (H100/A100) at Q4_K_M | 2× B200 192 GB or 8× H100 with FP8 weights |
Two patterns matter. First, the 24 GB tier is where serious local models start — everything from Mistral Small up to Qwen 3 32B lists a 24 GB card as its minimum. Second, the jump from "runs" to "runs well" is usually one tier up: a 32B model at Q4 on 24 GB works, but the same model at Q6 with longer context wants the 32 GB RTX 5090. Check the exact card you're considering against any model's fit list on its model page, or run your own numbers with the VRAM calculator.
Which GPU is best in each power and price bracket?
VRAM sets the ceiling, but your PSU and budget set what you can actually install. Here are the picks per bracket, using MSRP and datasheet TDP from our database.
| Bracket | Pick | Why | PSU guidance |
|---|---|---|---|
| Under $300 | Intel Arc B580 — $249 | Only new 12 GB card near this price; 190 W | 600 W PSU typical |
| $300–500 | Used RTX 3090 — street varies | 24 GB for the price of a midrange new card; 350 W | 750 W PSU, check 12V headroom |
| $400–550 (new) | RTX 5060 Ti 16GB — $429 | Cheapest new 16 GB; low 180 W draw | 600 W PSU typical |
| $700–800 | RTX 5070 Ti — $749 | Fastest bandwidth-per-dollar new 16 GB card; 300 W | 750 W PSU |
| $900–1,100 | RTX 5080 — $999 | Fastest 16 GB card (132 tok/s recorded); 360 W | 850 W PSU |
| ~$1,000 (AMD) | RX 7900 XTX — $999 | 24 GB + 960 GB/s; ROCm/Vulkan setup required; 355 W | 850 W PSU |
| $1,500–2,000 | RTX 5090 — $1,999 | 32 GB, 1,792 GB/s, fastest single-GPU inference; 575 W | 1,000 W PSU, ATX 3.x preferred |
| Workstation budget | RTX 6000 Ada / RTX PRO 6000 | 48–96 GB for comfortable 70B; pro drivers, ECC | Workstation chassis; 600 W card |
Power reality check: the jump from a 16 GB tier card (180–360 W) to the RTX 5090 (575 W) often includes a PSU replacement. Budget for it — a 575 W GPU on an undersized 650 W unit will trip protections under load.
NVIDIA vs AMD vs Intel for LLM inference
CUDA remains the default software target for local AI tooling, so NVIDIA cards have the fewest compatibility problems. AMD cards run llama.cpp well through ROCm and Vulkan — our RX 7900 XTX record of 76 tok/s was measured on ROCm — but some tools assume NVIDIA and need configuration. Intel Arc cards work through Vulkan with the youngest ecosystem of the three; fine for llama.cpp, rougher elsewhere. If you tinker, AMD's VRAM-per-dollar wins; if you want first-attempt success, stay NVIDIA. Full breakdown: AMD vs NVIDIA for AI.
How did we rank these GPUs?
Specifications come from manufacturer datasheet values stored in our GPU database (VRAM, memory type, bandwidth, TDP, launch MSRP). Token speeds come from our benchmark database, where every entry carries its source, software stack, and estimate flag — llama.cpp/llama-bench results on CUDA, ROCm, Metal, or Vulkan, plus Procyon and MLPerf-derived records where noted. We rank VRAM capacity first for LLM use, then bandwidth, then power draw, then price. We deliberately report MSRP only, never street prices, because those change constantly. Affiliate links do not influence rankings. Details: our methodology.
Frequently Asked Questions
How much VRAM do I need to run a 70B model locally?
About 40 GB at Q4 quantization before context overhead, which is why our model-fit database lists 48 GB cards like the RTX 6000 Ada as the minimum for Llama 3.x 70B and 80 GB as the recommendation for full context. A 32 GB RTX 5090 can run 70B at Q4 tightly — our Tom's Hardware-sourced record shows roughly 42 tok/s, flagged as an estimate — while 24 GB cards require heavy CPU offload and drop to unusable speeds.
Is the RTX 5090 overkill for local LLMs?
No, if you run 32B-class models or larger regularly — it is the only consumer card that holds them at higher quantizations with long context, and its 215 tok/s on 8B models makes small-model experimentation feel instant. Yes, if you only run 7B–8B models: those run at 38–85 tok/s on cards costing a quarter of the price, already faster than reading speed.
Is more VRAM or more bandwidth better for LLM inference?
VRAM first, bandwidth second. A model that doesn't fit in VRAM runs orders of magnitude slower from system RAM regardless of bandwidth. Once the model fits, generation speed scales close to linearly with bandwidth — the 1,792 GB/s RTX 5090 is why it beats the 936 GB/s RTX 3090 despite both fitting the same quantized models below 24 GB.
Is a used RTX 3090 still worth it for LLMs in 2026?
Yes — it remains the cheapest path to 24 GB, which our model-fit data shows is the minimum for the most capable local models (Gemma 3 27B, Qwen 3 32B, DeepSeek R1-Distill 32B). Accept the trade-offs: no warranty, unknown history, 350 W draw, and a 2020-era feature set. Our used RTX 3090 buying guide covers how to evaluate individual cards.
Can I run LLMs on multiple cheaper GPUs instead of one big one?
Yes, with caveats. Two 24 GB cards give 48 GB total, enough for 70B at Q4 — but model layers split across a PCIe link without NVLink run slower than on one large card, and not all tools split cleanly. Our multi-GPU setup guide covers which configurations work. For most buyers, one card whose VRAM fits the model beats two smaller ones.
Do I need an RTX PRO 6000 or H100 for local AI?
Only above the 70B single-card ceiling or for multi-user serving. A single RTX PRO 6000 Blackwell (96 GB) runs 70B at high quantizations with long context; H100/A100 systems earn their cost when many users share one deployment. Hobbyist and single-user workloads through 32B fit comfortably on consumer tiers.
Sources
Specifications are manufacturer datasheet values from our GPU database. Benchmark figures are from the sources below, as recorded with per-entry attribution in our benchmark database.
- llama.cpp / llama-bench community benchmarks — https://github.com/ggml-org/llama.cpp
- Tom's Hardware GPU benchmarks — https://www.tomshardware.com/pc-components/gpus
- MLCommons MLPerf Inference — https://mlcommons.org/benchmarks/inference-datacenter/
- NVIDIA official specifications — https://www.nvidia.com/en-us/data-center/
- AMD Instinct specifications — https://www.amd.com/en/products/accelerators/instinct.html
- Apple Mac Studio technical specifications — https://www.apple.com/mac-studio/specs/
Related reading: our ranked shortlist in Best GPU for Local LLMs, the VRAM calculator, per-model hardware fit pages in the model index, and the deep learning training guide. For direct head-to-head data, see our editorial comparisons: RTX 5090 vs 4090, RTX 5090 vs 5080, RTX 5080 vs 4090, and RTX 5090 vs RX 7900 XTX — or build any other pairing in the GPU comparison tool.
Disclosure: CompareAIHardware.com participates in the Amazon Associates program and earns from qualifying purchases through links on this page. Affiliate relationships do not influence our recommendations.
Head-to-head comparisons from this guide
Every pick above has a dedicated deep-dive against its closest rival:
- RTX 5090 vs RTX 4090 — is the new halo card worth it for inference?
- RTX 5090 vs RTX 5080 — 32 GB vs 16 GB at the top of the stack
- RTX 5080 vs RTX 4090 — fastest 16 GB vs cheapest current 24 GB
- RTX 5070 Ti vs RTX 4080 SUPER — the two value picks in the 16 GB tier
- RX 7900 XTX vs RTX 4090 — AMD's 24 GB answer to NVIDIA
- RTX 5090 vs RX 7900 XTX — flagship CUDA vs flagship ROCm
- Arc B580 vs RX 7600 XT — budget 12 GB vs 16 GB