Best GPU for Llama 4 Scout 109B-A17B Locally
Real-time prices and hardware recommendations updated for August 2026.
Llama 4 Scout 109B-A17B is a mixture-of-experts model: all 109B parameters must sit in VRAM, but only 17B activate per token — so it generates far faster than a dense model of the same size, while still demanding the memory of one.
To run Llama 4 Scout 109B-A17B locally you need roughly 67 GB of VRAM at Q4_K_M quantization with a 64k token context. The best-value card that fits is the RTX PRO 6000 Blackwell (96 GB), which should generate around 44 tokens per second.
Adjust Context Length
Slide to popular sizes or type any value manually.
Recommended Hardware
Q3 weights (~47GB) plus 64k KV cache exceed 48GB. The RTX PRO 6000 Blackwell (96GB) is the only single-GPU option at this tier.
Recommended Hardware
Q4 weights are ~63GB. Needs high-end workstation or Mac Studio Ultra-class hardware.
Recommended Hardware
No fitting GPUs found in database. Browse all GPUs
Q8_0 requires ~130GB VRAM for this 109B model — no single consumer or workstation GPU can accommodate it. Consider a multi-GPU cluster or Apple Silicon with unified memory (Mac Studio Ultra).
Other models with the same VRAM requirement
Because Llama 4 Scout 109B-A17B's weights fit a 96 GB card, other models of a similar size run on the same GPU. Generation speed varies — mixture-of-experts models are faster, dense models slower — but any of these load in the same VRAM:
Optimizing Setup for Llama 4 Scout 109B-A17B
Quantization Recommendations
For daily coding and reasoning tasks, Q4_K_M (4-bit quantization) offers the best balance of quality and memory efficiency — it reduces memory requirements by over 70% with minimal quality loss compared to FP16. Q8 and higher presets preserve more fidelity at the cost of significantly higher VRAM usage, which may force layer offloading and hurt throughput.
Recommended Local Software
We recommend using Ollama as the primary runner for local inference due to its automated GPU model splitting and context cache optimizations. For advanced fine-tuning or quantization splits, llama.cpp with Flash Attention compiled natively provides the best granular control.
Running Llama 4 Scout 109B-A17B locally — FAQ
How much VRAM do I need to run Llama 4 Scout 109B-A17B?
At a 64k context with KV cache quantization on, Llama 4 Scout 109B-A17B needs about 67 GB of VRAM at Q4_K_M — the quantization most people should use. Dropping to Q3_K_M brings that down to roughly 51.4 GB at some quality cost, while Q8_0 needs about 129.7 GB for the best quality this model can give.
What size graphics card does Llama 4 Scout 109B-A17B fit on?
Llama 4 Scout 109B-A17B needs about 67 GB at Q4_K_M, which is more than a single 24 GB consumer card provides. You need a workstation card, a multi-GPU setup, or a more aggressive quantization — otherwise layers spill into system RAM and generation slows dramatically.
Which quantization should I use for Llama 4 Scout 109B-A17B?
Use Q4_K_M unless you have VRAM to spare. It needs about 67 GB and loses very little quality against full precision. Q8_0 needs about 129.7 GB for a quality gain most people cannot detect in everyday coding and chat. Spend spare VRAM on a longer context instead.
How does context length affect the VRAM Llama 4 Scout 109B-A17B needs?
Model weights are fixed, but the KV cache grows linearly with context. For Llama 4 Scout 109B-A17B at a 64k context the cache is about 4.4 GB; doubling to 128k takes it to roughly 8.7 GB. Turning KV cache quantization off doubles those figures again.
Why is Llama 4 Scout 109B-A17B faster than its parameter count suggests?
Llama 4 Scout 109B-A17B is a mixture-of-experts model. Its 109B parameters all have to be held in VRAM, but only 17B are used to produce each token. Generation speed is bound by streaming those 17B active parameters, so it feels much closer to a 17B model than a 109B one — while still needing memory for the full 109B.
Can the same GPU run other models similar to Llama 4 Scout 109B-A17B?
Yes. Llama 4 Scout 109B-A17B needs about 67 GB at Q4_K_M, and any model whose weights fit the same card runs on it — including Qwen3.5-122B-A10B (122B), DBRX (132B), Mixtral 8x22B (141B). The weights fit the same GPU; generation speed varies (mixture-of-experts models are faster, dense models slower).
▸How token speeds are estimated
Two metrics are shown per GPU: Read tok/s (how fast the model ingests your prompt) and Decode tok/s (how fast it streams tokens back). They model fundamentally different bottlenecks.
Read (Prefill)
The prompt is processed in one parallel pass. This is compute-bound: it saturates the GPU's tensor cores.
Decode (Generation)
Each new token requires loading the entire model's active weights from VRAM. This is memory-bandwidth-bound: the GPU stalls waiting for data, not computing.
Weights = (activeParams × bits ÷ 8) × 1.15 overhead. KV cache per step = activeParams × multiplier × contextK.
Architecture utilization factors
Left: decode factor — Right: read factor
Data sources
TFLOPS and memory bandwidth are read from the GPU database. When missing, bandwidth falls back to a hardcoded dictionary.
Limitations
These are analytical estimates, not benchmark results. Use them as a relative comparison, not an absolute performance guarantee.