Models / Flint 40B-A2.5B
Best home rig for Flint 40B-A2.5B
Current model39.8B total · 2.5B active6 measured resultsfastest plain decode: 179.4 tok/s model info ↗
Will it fit my GPU? Best value GPU per euro →
Real, measured generation speeds for Flint 40B-A2.5B on home hardware, fastest first. Different quants/engines aren't directly comparable.
Memory math: how much VRAM / RAM does Flint 40B-A2.5B need?
Flint 40B-A2.5B has 39.8B parameters, about 2.5B active per token (source: 39.8B total / ~2.5B active MoE coding model (Recursive, Apache 2.0), per Hugging Face model card). Weight size = parameters × bits-per-weight ÷ 8, before context (KV cache) and runtime overhead.
| Format | Bits/weight | Weights | Smallest common memory size that holds the weights alone |
|---|---|---|---|
| Q8_0 | 8.50 | 42.3 GB | 48 GB |
| Q6_K | 6.56 | 32.7 GB | 48 GB |
| Q5_K_M | 5.70 | 28.4 GB | 32 GB |
| Q4_K_M | 4.89 | 24.3 GB | 32 GB |
| Q3_K_M | 4.00 | 19.9 GB | 24 GB |
| Q2_K | 3.16 | 15.7 GB | 16 GB |
Bits-per-weight for GGUF quants are llama.cpp's own figures (llama.cpp quantize README). "As measured" rows apply the source's stated bits-per-weight to the parameter count; real files differ slightly because some layers stay at higher precision. Try other combinations in the VRAM calculator.
Why it can still run on smaller GPUs: it is a mixture-of-experts model, so only about 2.5B of its 39.8B parameters are used for each generated token. Runtimes such as llama.cpp (--n-cpu-moe), ExLlamaV3 or Project Maya can keep most expert weights in system RAM and only the shared layers and hot experts in VRAM. Then you need VRAM + system RAM ≥ weights, and speed is limited mainly by RAM bandwidth.
Measured on setups with less GPU memory than the Q4_K_M weights (so part of the model lived in system RAM or was streamed from disk):
- RTX 4090 (24 GB) + 504 GB: 179.4 tok/s (NVFP4)
- RTX 4090 (24 GB) + 504 GB: 169.2 tok/s (UD-Q3_K_XL)
- RTX 3090 (24 GB) + 1008 GB: 128.9 tok/s (UD-Q3_K_XL)
FAQ
How much VRAM does Flint 40B-A2.5B need?
The weights are about 24.3 GB at Q4_K_M (39.8B params × 4.89 bits ÷ 8, before context/KV cache). Add memory for context (KV cache) and the runtime. The smallest-memory setup with a measured result here is RTX 4090 (24 GB) at 179.4 tok/s (NVFP4). The measured runs on it used NVFP4 / UD-Q3_K_XL, not Q4_K_M. At Q4_K_M the weights are about 24.3 GB, more than 24 GB, so Q4_K_M doesn't fully fit without offloading part of the model to system RAM.
What is the fastest measured hardware for Flint 40B-A2.5B?
RTX 4090: 179.4 tok/s generation (llama.cpp 0.7.0-dev (build 11803), NVFP4), from https://llm-speed.com/r/r_sw92j6kg7js.
Which GPUs have measured results for Flint 40B-A2.5B?
Apple M4 Pro, RTX 3090, RTX 4090.
Can it run on… Apple M4 Pro · RTX 3090 · RTX 4090
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