🧠 Best GPU for Running Local LLMs 2026
Ranked for Ollama, llama.cpp and LM Studio — where VRAM decides what you can run and bandwidth decides how fast.
Running an LLM locally is a memory problem before it is a speed problem. VRAM decides which models exist for you at all, and memory bandwidth — not raw compute — sets how fast they generate. This list is ranked accordingly.
💡 24GB is the sweet spot: it runs the 32B class at Q4 with real context. Below 12GB you are limited to 8B models.
What each VRAM tier actually runs
Rule of thumb at Q4_K_M quantization: roughly 0.6GB per billion parameters, plus KV cache, plus 10-20% runtime overhead. The jumps are what matter — going from 16GB to 24GB unlocks a whole model class, while 24GB to 32GB mostly buys context length.
| VRAM | What you can realistically run | Notes |
|---|---|---|
| 8 GB | Llama 3.1 8B or Qwen3 8B at Q4_K_M (~5.5GB loaded), 8-16K context | Hard ceiling around 8B. A 13B needs Q3 or offloading. |
| 12 GB | 8B with long context, or a 14B at Q4_K_M (Phi-4 14B, Qwen3 14B) | The practical floor for a genuinely useful coding assistant. |
| 16 GB | 14B at Q4/Q5 comfortably, or a 20B-class model | A dense 27B still spills to system RAM. |
| 24 GB | Qwen3 32B at Q4_K_M (~19-20GB) or Gemma-3-27B with real context | The sweet spot. 70B only at Q3 with tiny context — unpleasant. |
| 32 GB | The same 32B class but at 100K+ context, or 27-35B at Q5/Q6 | Over a 24GB card this buys context headroom, not a new model class. |
| 48 GB+ | Dense 70B at Q4 (Llama-3.3-70B Q4_K_M is 42.5GB of weights alone) | Two 24GB cards is the only realistic home route to dense 70B. |
Context length inflates VRAM more than people expect
The KV cache grows linearly with context and often ends up larger than the quantized weights. An 8B model that fits comfortably in 8GB at 4K context needs roughly 12-16GB at 128K. Using --cache-type-k q8_0 roughly halves it. Two caveats: mixture-of-experts models do not reduce VRAM (every expert must be resident, MoE only cuts compute per token), and newer sliding-window attention models break the uniform-layer maths below, so these figures over-estimate them.
| Model | KV cache per 1K tokens | At 32K | At 128K |
|---|---|---|---|
| Llama 3.1 8B | ~0.13 GB | 4.0 GB | 16.8 GB |
| 32B class (64 layers) | ~0.26 GB | 8.0 GB | — |
| Llama 3.1 70B | ~0.31 GB | 10 GB | — |
Token generation is bandwidth-bound — and that changes the ranking
Generating a token is a matrix-vector operation with an arithmetic intensity of roughly 1-2 FLOPs per byte, against a hardware ridge point around 206. Every single token re-reads the active weights and the whole KV cache out of VRAM, so bandwidth is the ceiling — cutting compute in half raises decode latency only about 22%. Prompt processing is the opposite: that phase is compute-bound. The measured numbers below show why the distinction matters, and almost no GPU comparison separates them.
| GPU | Bandwidth | Generation (tok/s) | Prompt processing |
|---|---|---|---|
| RTX 5090 | 1792 GB/s | 300 | 14,970 |
| RTX 4090 | 1008 GB/s | 190 | 14,771 |
| RX 7900 XTX | 960 GB/s | 195 | ~3,800 |
| RTX 3090 | 936 GB/s | 162 | 5,560 |
| Arc B580 | 456 GB/s | 72 | 621 |
| Arc A770 | 560 GB/s | 53 | 1,120 |
Read that table twice
The RX 7900 XTX generates tokens slightly faster than an RTX 4090 (195 vs 190/s) because decode tracks bandwidth and the two cards are close there. But it processes prompts roughly four times slower, because that phase needs tensor cores. So if you chat with short prompts, AMD is genuinely competitive. If you do RAG, long documents or agentic work with big prompts, the NVIDIA card is in a different league. Likewise the RTX 3090 nearly matches a 4090 on generation while delivering about a third of its prompt throughput — a detail that decides whether a used 3090 is a bargain or a trap for your particular workload.
AMD and Intel in 2026 — the old advice is out of date
ROCm 7.x is a unified Windows and Linux release, the official Ollama container auto-detects RDNA3 and RDNA4, and the HSA_OVERRIDE_GFX_VERSION hacks are gone. AMD is a real option now. It is not free, though: one test set found 92% of workloads ran on a 4090 with zero configuration changes, against 68% needing at least one intervention on a 7900 XTX, and Windows remains the weak spot. One genuinely surprising finding, still an open llama.cpp issue: on RDNA3 the Vulkan backend often beats ROCm for token generation while ROCm wins prompt processing — so keep both builds rather than assuming one is correct. For Intel, note that IPEX-LLM was archived in January 2026; support now lives upstream in PyTorch, and plain llama.cpp Vulkan frequently outruns the Intel-specific stack.
Beyond consumer cards: workstation and datacenter accelerators
Everything ranked above is a card you can buy at a normal retailer. Once you need more than 32GB in one machine the list changes completely, and so does the price. Read the bandwidth column first: decode speed tracks it almost linearly, so a 1,344 GB/s card generates tokens roughly three times faster than a 456 GB/s one on a model that fits in both. Then read the price column, because the spread there is far wider than the performance spread. These cards are not in the ranking above — we have no measured benchmark for them, and this site does not rank hardware on estimated numbers. Every figure below is a vendor-published spec or a verified retail price.
| Card | VRAM | Bandwidth | Power | Typical price | What it is for |
|---|---|---|---|---|---|
| AMD Instinct MI300X | 192 GB HBM3 | 5,325 GB/s | 750 W | ~$10-15k, or ~$3/hr rented | OAM module, not a PCIe card — realistically a rental. Runs 70B with room to spare and 120B-class MoE on one device. |
| NVIDIA RTX PRO 6000 Blackwell | 96 GB GDDR7 ECC | 1,792 GB/s | 600 W | $8,565 launch → ~$13,250 | The largest VRAM pool on any card you can put in a desktop. 600W and a flow-through cooler make multi-card builds impractical. |
| RTX PRO 6000 Blackwell Max-Q | 96 GB GDDR7 ECC | 1,792 GB/s | 300 W | ~$9,700 | Same silicon and same bandwidth at half the power, blower cooler. This is the multi-GPU variant. |
| NVIDIA RTX PRO 5000 Blackwell 72GB | 72 GB GDDR7 ECC | 1,344 GB/s | 300 W | Integrator / OEM | Capacity-per-watt pick: dense 70B at Q4 with real context, in the same power envelope as an RTX 6000 Ada. |
| NVIDIA RTX 6000 Ada | 48 GB GDDR6 ECC | 960 GB/s | 300 W | Previous gen — resale | Superseded by the PRO 5000. No native FP4, and the 72GB Blackwell beats it on every axis that matters here. |
| NVIDIA L40S | 48 GB GDDR6 ECC | 864 GB/s | 350 W | Server SKU only | Passively cooled, no display outputs, needs server airflow. Ada tensor cores with FP8 — strong per dollar if you already have the chassis. |
| AMD Radeon PRO W7900 | 48 GB GDDR6 ECC | 864 GB/s | 295 W | ~$3,500 (Dual Slot) | The cheapest new 48GB unified pool. ROCm, so expect setup work — and see the AMD notes above before committing. |
| Intel Arc Pro B60 Dual 48GB | 2 × 24 GB GDDR6 | 456 GB/s per GPU | ~400 W (2 GPUs) | ~$1,200 | Two separate GPUs on one board — NOT a 48GB unified pool. A 40GB model does not fit; two 20GB models do. |
| NVIDIA RTX A6000 (Ampere) | 48 GB GDDR6 ECC | 768 GB/s | 300 W | Used market | Two generations old and no FP8. Only interesting if the used price drops below a pair of consumer 24GB cards. |
| AMD Radeon AI PRO R9700 | 32 GB GDDR6 | 640 GB/s | 300 W | $1,299 | An RX 9070 XT with double the memory in clamshell and a blower. Cheapest new 32GB card; bandwidth is its weak axis. |
| NVIDIA RTX PRO 4500 Blackwell | 32 GB GDDR7 ECC | ~896 GB/s | 200 W | Integrator / OEM | 32GB at 200W, dual-slot. A 165W single-slot passive server edition exists for dense multi-card nodes. |
| NVIDIA RTX PRO 4000 Blackwell | 24 GB GDDR7 ECC | 672 GB/s | 145 W | from ~$2,089 | Single-slot, 145W, full height. Its reason to exist is fitting 24GB into a small or already-full workstation. |
| Intel Arc Pro B60 24GB | 24 GB GDDR6 | 456 GB/s | 200 W | $599-799 | Cheapest new 24GB card by a wide margin. A used RTX 3090 costs about the same with double the bandwidth and CUDA. |
Four things the professional spec sheet does not tell you
First, capacity printed on a box is not always one pool. The Arc Pro B60 Dual advertises 48GB but is two 24GB GPUs on a shared board, so a single 40GB model will not load — the same caveat applies to any multi-GPU total, including two 24GB consumer cards. Second, professional pricing has decoupled from MSRP: the RTX PRO 6000 Blackwell launched at $8,565 and sits around $13,250 as of August 2026, driven by the GDDR7 shortage rather than anything about the card. Third, cooling decides whether a card is usable at all — the L40S and the server editions are passive and simply overheat in a desktop, while the 600W workstation variant exhausts into the room and cannot be stacked. Fourth, ECC memory and certified drivers are a large part of what you are paying for, and neither makes a local model run faster. If your workload is inference and your tolerance for a crash is normal, a consumer card at the same VRAM is usually the better buy.
Best GPU for Running Local LLMs — 2026 Guide
Running an LLM locally is a memory problem before it is a speed problem. VRAM decides which models exist for you at all, and memory bandwidth — not raw compute — sets how fast they generate. This list is ranked accordingly.
24GB is the sweet spot: it runs the 32B class at Q4 with real context. Below 12GB you are limited to 8B models.
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