Qwen3.8-27B-Turbo-Brilliance-50X-Reasoning-Instruct-NEO-MAX-MTP requirements — can you run it?
~27.0B parameters · 13 quantizations measured · updated 2026-10-13 · source: DavidAU/Qwen3.8-27B-Turbo-Brilliance-50X-Reasoning-Instruct-NEO-MAX-MTP-GGUF
# llama.cpp (auto-downloads the GGUF) llama-cli -hf DavidAU/Qwen3.8-27B-Turbo-Brilliance-50X-Reasoning-Instruct-NEO-MAX-MTP-GGUF:IQ2_M # Ollama (pulls straight from Hugging Face) ollama run hf.co/DavidAU/Qwen3.8-27B-Turbo-Brilliance-50X-Reasoning-Instruct-NEO-MAX-MTP-GGUF:IQ2_M # LM Studio: search "DavidAU/Qwen3.8-27B-Turbo-Brilliance-50X-Reasoning-Instruct-NEO-MAX-MTP-GGUF" in the model browser
Commands load the exact quant measured on this page.
Every quantization: real file size vs what you actually need
| Quant | File (measured) | VRAM/RAM needed (8K ctx) | Runs on (first fit) |
|---|---|---|---|
| IQ2_M | 12.1 GB | 16.0 GB | RTX 3090 24GB |
| IQ3_M | 14.5 GB | 18.9 GB | RTX 3090 24GB |
| IQ2_XS | 17.2 GB | 22.1 GB | RTX 5090 32GB |
| Q4_K_S | 17.5 GB | 22.5 GB | RTX 5090 32GB |
| IQ2_S | 18.9 GB | 24.1 GB | RTX 5090 32GB |
| IQ3_XXS | 20.5 GB | 26.1 GB | RTX 5090 32GB |
| Q5_K_S | 20.6 GB | 26.3 GB | RTX 5090 32GB |
| Q5_K_M | 21.2 GB | 26.9 GB | RTX 5090 32GB |
| IQ3_S | 23.9 GB | 30.2 GB | RTX PRO 6000 96GB |
| IQ4_XS | 47.4 GB | 58.4 GB | RTX PRO 6000 96GB |
| Q4_K_M | 51.9 GB | 63.7 GB | RTX PRO 6000 96GB |
| Q8_0 | 87.9 GB | 107.0 GB | Mac 128GB unified |
| Q6_K | 92.1 GB | 112.1 GB | Mac 128GB unified |
KV cache uses a flat +20% context/overhead rule because this repo does not publish its config; exact numbers may differ for long contexts. MoE models keep all experts in memory — total size counts, not just active params. Longer context grows the KV cache — use the selector above.
Can I run Qwen3.8-27B-Turbo-Brilliance-50X-Reasoning-Instruct-NEO-MAX-MTP on my GPU? (IQ2_M recommended quant)
| Hardware | Memory | Verdict | Est. speed |
|---|---|---|---|
| RTX 3060 12GB | 12 GB | no | |
| RTX 4060 Ti 16GB | 16 GB | no | |
| RTX 3090 24GB | 24 GB | RUNS | ~58 tok/s |
| RTX 4090 24GB | 24 GB | RUNS | ~62 tok/s |
| RTX 5090 32GB | 32 GB | RUNS | ~111 tok/s |
| RTX PRO 6000 96GB | 96 GB | RUNS | ~111 tok/s |
| Mac 16GB unified | 16 GB | no | |
| Mac 32GB unified | 32 GB | RUNS | ~9 tok/s |
| Mac 64GB unified | 64 GB | RUNS | ~17 tok/s |
| Mac 128GB unified | 128 GB | RUNS | ~25 tok/s |
| Mac 256GB unified | 256 GB | RUNS | ~34 tok/s |
| Mac 512GB unified | 512 GB | RUNS | ~51 tok/s |
| 32GB system RAM (CPU) | 32 GB | RUNS | ~3 tok/s |
| 64GB system RAM (CPU) | 64 GB | RUNS | ~3 tok/s |
| 128GB system RAM (CPU) | 128 GB | RUNS | ~5 tok/s |
| 256GB system RAM (CPU) | 256 GB | RUNS | ~5 tok/s |
| 1TB server RAM (CPU) | 1024 GB | RUNS | ~12 tok/s |
| 2TB server RAM (CPU) | 2048 GB | RUNS | ~12 tok/s |
Speed = decode tok/s estimated from memory bandwidth ÷ measured file size (× MoE active share). Real numbers vary ±30% by runtime and settings.
How much VRAM does Qwen3.8-27B-Turbo-Brilliance-50X-Reasoning-Instruct-NEO-MAX-MTP need?
The honest answer: 16.0 GB at the IQ2_M quant with 8K context — that is the measured
file size (12.1 GB) plus KV cache and runtime overhead. The smallest published build needs
16.0 GB; the lossless one needs 112.1 GB.
Cheapest hardware that runs it: Used RTX 3090 24GB (~$650, used market) — see which GPU to buy.
Can I run it with CPU offload?
Yes, if combined RAM+VRAM ≥ file size — but generation speed is limited by memory bandwidth. Expect roughly: DDR5 dual channel ~10-30 tok/s for small MoE actives, single digits for big ones, 0.1-1 tok/s when experts page from disk. CPU-only is fine for batch jobs, painful for chat.
Why do other sites show different numbers?
Most pages compute weights from a formula (params × bits/8). We use the actual file sizes published in GGUF repos, which include the embedding table, unquantized tensors, and container overhead — that is why our numbers can differ from a naive calculation.
FAQ
How much VRAM does Qwen3.8-27B-Turbo-Brilliance-50X-Reasoning-Instruct-NEO-MAX-MTP need?
The measured answer: 16.0 GB at the IQ2_M quant with 8K context. The smallest published build needs 16.0 GB; the lossless (F16/BF16) build needs 112.1 GB. These are real GGUF file sizes from Hugging Face, not formula estimates.
Can I run Qwen3.8-27B-Turbo-Brilliance-50X-Reasoning-Instruct-NEO-MAX-MTP on a 24GB GPU (RTX 3090/4090)?
Yes — at IQ2_M (16.0 GB). Quants that fit 24GB: IQ2_M, IQ3_M, IQ2_XS, Q4_K_S.
How fast will Qwen3.8-27B-Turbo-Brilliance-50X-Reasoning-Instruct-NEO-MAX-MTP run?
Decode speed is memory-bandwidth-bound. At IQ2_M: roughly 62 tok/s on an RTX 4090, 58 tok/s on an RTX 3090, 25 tok/s on an M-series Mac with 128GB, and 3.1 tok/s on CPU with dual-channel DDR4 — estimates from measured file size, MoE active-parameter share and memory bandwidth.
Can I run Qwen3.8-27B-Turbo-Brilliance-50X-Reasoning-Instruct-NEO-MAX-MTP on CPU with system RAM?
Yes, if your usable RAM is at least the file size. At IQ2_M you need ~16.0 GB of RAM. CPU generation is memory-bandwidth-bound: expect single-digit tok/s for large MoE models, 10-30 tok/s for small active params, and below 1 tok/s when experts page from disk.
Why do VRAM numbers for Qwen3.8-27B-Turbo-Brilliance-50X-Reasoning-Instruct-NEO-MAX-MTP differ between sites?
Most sites compute weights from a formula (params x bits/8). ModelFit uses the actual GGUF file sizes published on Hugging Face, which include the embedding table, unquantized tensors, and container overhead — so our numbers reflect what you really download and load.