Best local LLM for 256 GB.
Qwen3.8-Flash-Next (125B-A6B) is the smartest model that fits in 256 GB. It needs about 140 GB and is about 69% as smart as Claude Opus 5.5.
| Model | Maker | Score (AA) | Needs 4-bit | Needs 8-bit | Run it |
|---|---|---|---|---|---|
| Qwen3.8-Flash-Next (125B-A6B) | Alibaba Qwen | 39.8 | 140 GB | 219.3 GB | ollama run qwen3.8-flash-next:125b-a6b |
| Qwen3.8 27B | Alibaba Qwen | 33.7 | 22.7 GB | 36.5 GB | ollama run qwen3.8:27b |
| Nemotron 3.5 Super VL 120B-A12B | NVIDIA | 26.5 | 105.5 GB | 154.9 GB | ollama run nemotron-3.5-super:120b-a12b |
| K2 Horizon MoVA 36B-A4B | IFM (MBZUAI) | 25.3 | 28.1 GB | 47.8 GB | MLX only (Mac) |
| Qwen3.5 27B | Alibaba Qwen | 22.9 | 21.5 GB | 36.5 GB | ollama run qwen3.5:27b |
| MiniMax M2.7 | MiniMax | 22.8 | 161.1 GB | 281.6 GB | MLX only (Mac) |
Pick by what you do.
CodingQwen3.8-Flash-Next (125B-A6B)
Needs 140 GB. Terminal-Bench 2.1: 86.1%.
ollama run qwen3.8-flash-next:125b-a6bWhat 256 GB adds over 192 GB.
| Model | Needs | Score (AA) |
|---|---|---|
| MiniMax M2.7 | 161.1 GB | 22.8 |
Computers and cards with 256 GB.
Graphics cards count VRAM, so they often fit a bit more.
| Machine | Type | Usable | Models that fit | Best fit | From (approx) |
|---|---|---|---|---|---|
| Mac Studio (M5 Ultra) | Mac desktop | 192 GB | 95 | Qwen3.8-Flash-Next (125B-A6B) | $5,499 · 2026-10-15 |
Short answers.
- What is the best local LLM for 256 GB of RAM?
- Qwen3.8-Flash-Next (125B-A6B) by Alibaba Qwen. It scores 39.8 on the Artificial Analysis Intelligence Index and needs about 140 GB at 4-bit. That is about 69% of Claude Opus 5.5 (57.6), the top paid model. Data as of 2026-10-15.
- How many local models fit in 256 GB?
- 95 of the 124 text models in our data fit. We assume about 192 GB is usable for the model (75% of 256 GB), and a model needs its 4-bit file size × 1.15 + 2 GB.
- What is the biggest model that runs in 256 GB?
- MiniMax M2.7, 228.7B parameters, needs about 161.1 GB at 4-bit. Bigger is not always smarter: Qwen3.8-Flash-Next (125B-A6B) scores 39.8; MiniMax M2.7 scores 22.8.
- What is the best coding model for 256 GB?
- Qwen3.8-Flash-Next (125B-A6B) (needs 140 GB, Terminal-Bench 2.1 86.1%). Run it in a coding app like OpenCode or Cline. See https://insidethemachine.ai/best/local-coding-model.
- Does 256 GB of VRAM work the same as 256 GB of RAM?
- Close. On a Mac the memory is shared, and about 75% goes to the model. On a PC the graphics card's VRAM is what counts; we estimate VRAM minus 1 GB is usable, so a 256 GB card fits a little more than 256 GB of Mac memory. Models that spill out of VRAM into normal RAM run much slower.
Scores are from Artificial Analysis and LMArena, not our own tests yet. Memory need = 4-bit file × 1.15 + 2 GB; a model fits when that is at most 75% of memory. Models 2026-10-15, machines 2026-10-15.
# Best local LLM for 256GB RAM or VRAM URL: https://insidethemachine.ai/best/local-llm-for-256gb-ram Data: models 2026-10-15, machines 2026-10-15. Source of scores: Artificial Analysis Intelligence Index (AA), LMArena. Not our own tests yet. Usable memory assumed: 192 GB (75% of 256). Rule: min_memory_gb = weights_gb_4bit × 1.15 + 2. weights_gb_4bit = Q4_K_M file (Ollama, else HF GGUF) → MLX 4-bit (HF) → Ollama MLX/NVFP4 tag → Ollama default tag → params_total_b × 0.6 (flagged weights_estimated). +15% ≈ KV cache at ~8–16K context; +2GB runtime/process overhead. Long contexts (128K+) need much more. Models that fit: 95 of 124. ## Top picks | model | maker | AA index | needs (4-bit) | needs (8-bit) | run | |---|---|---|---|---|---| | Qwen3.8-Flash-Next (125B-A6B) | Alibaba Qwen | 39.8 | 140 GB | 219.3 GB | `ollama run qwen3.8-flash-next:125b-a6b` | | Qwen3.8 27B | Alibaba Qwen | 33.7 | 22.7 GB | 36.5 GB | `ollama run qwen3.8:27b` | | Nemotron 3.5 Super VL 120B-A12B | NVIDIA | 26.5 | 105.5 GB | 154.9 GB | `ollama run nemotron-3.5-super:120b-a12b` | | K2 Horizon MoVA 36B-A4B | IFM (MBZUAI) | 25.3 | 28.1 GB | 47.8 GB | MLX only | | Qwen3.5 27B | Alibaba Qwen | 22.9 | 21.5 GB | 36.5 GB | `ollama run qwen3.5:27b` | | MiniMax M2.7 | MiniMax | 22.8 | 161.1 GB | 281.6 GB | MLX only | ## By job - Coding: Qwen3.8-Flash-Next (125B-A6B) (140 GB, Terminal-Bench 2.1 86.1%) `ollama run qwen3.8-flash-next:125b-a6b` - Agents (tool use): Qwen3.8-Flash-Next (125B-A6B) (140 GB) `ollama run qwen3.8-flash-next:125b-a6b` - Vision (reads images): Qwen3.8-Flash-Next (125B-A6B) (140 GB) ## New at 256 GB (did not fit in 192 GB) - MiniMax M2.7 (161.1 GB, AA 22.8) ## Machines with 256 GB | machine | type | usable GB | models that fit | best fit | from (approx) | |---|---|---|---|---|---| | Mac Studio (M5 Ultra) | Mac desktop | 192 | 95 | Qwen3.8-Flash-Next (125B-A6B) | $5,499 (2026-10-15) | ## Questions **What is the best local LLM for 256 GB of RAM?** Qwen3.8-Flash-Next (125B-A6B) by Alibaba Qwen. It scores 39.8 on the Artificial Analysis Intelligence Index and needs about 140 GB at 4-bit. That is about 69% of Claude Opus 5.5 (57.6), the top paid model. Data as of 2026-10-15. **How many local models fit in 256 GB?** 95 of the 124 text models in our data fit. We assume about 192 GB is usable for the model (75% of 256 GB), and a model needs its 4-bit file size × 1.15 + 2 GB. **What is the biggest model that runs in 256 GB?** MiniMax M2.7, 228.7B parameters, needs about 161.1 GB at 4-bit. Bigger is not always smarter: Qwen3.8-Flash-Next (125B-A6B) scores 39.8; MiniMax M2.7 scores 22.8. **What is the best coding model for 256 GB?** Qwen3.8-Flash-Next (125B-A6B) (needs 140 GB, Terminal-Bench 2.1 86.1%). Run it in a coding app like OpenCode or Cline. See https://insidethemachine.ai/best/local-coding-model. **Does 256 GB of VRAM work the same as 256 GB of RAM?** Close. On a Mac the memory is shared, and about 75% goes to the model. On a PC the graphics card's VRAM is what counts; we estimate VRAM minus 1 GB is usable, so a 256 GB card fits a little more than 256 GB of Mac memory. Models that spill out of VRAM into normal RAM run much slower. More: https://insidethemachine.ai/models?tier=256 · https://insidethemachine.ai/machines?tier=256 · https://insidethemachine.ai/best