Best GPU for LLAMA-4-SCOUT-109B-A17B Locally
Real-time prices and hardware recommendations updated for July 2026.
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).
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.
▸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.