# Best local LLM for 128GB RAM or VRAM

URL: https://insidethemachine.ai/best/local-llm-for-128gb-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: 96 GB (75% of 128). 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: 90 of 124.

## Top picks

| model | maker | AA index | needs (4-bit) | needs (8-bit) | run |
|---|---|---|---|---|---|
| Qwen3.8 27B | Alibaba Qwen | 33.7 | 22.7 GB | 36.5 GB | `ollama run qwen3.8:27b` |
| 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` |
| Qwen3.6 27B | Alibaba Qwen | 21.4 | 21.5 GB | 36.5 GB | `ollama run qwen3.6:27b` |
| Nemotron 3.5 Bolt 74B-A9B | NVIDIA | 21.4 | 30.7 GB | - | `ollama run nemotron-3.5-bolt:74b-a9b` |
| K2 Horizon 7B | IFM (MBZUAI) | 20.6 | 8.6 GB | 13 GB | MLX only |

## By job

- Coding: Qwen3.8 27B (22.7 GB, Terminal-Bench 2.1 79.8%) `ollama run qwen3.8:27b`
- Agents (tool use): Qwen3.8 27B (22.7 GB) `ollama run qwen3.8:27b`
- Vision (reads images): Qwen3.8 27B (22.7 GB)

## New at 128 GB (did not fit in 96 GB)

- Qwen3.5 122B-A10B (95.1 GB, AA 17.7)
- Mistral Medium 3.5 128B (94 GB, AA 14.2)
- gpt-oss-120b (74.2 GB, AA 11.6)
- Mistral Small 4 119B (85 GB, AA 11.3)
- GLM-4.5-Air 106B-A12B (85.9 GB, AA 11.1)

## Machines with 128 GB

| machine | type | usable GB | models that fit | best fit | from (approx) |
|---|---|---|---|---|---|
| MacBook Pro 14"/16" (M5 Max) | Mac laptop | 96 | 90 | Qwen3.8 27B | $4,099 (2026-10-15) |
| Mac Studio (M5 Max) | Mac desktop | 96 | 90 | Qwen3.8 27B | $2,499 (2026-10-15) |
| NVIDIA DGX Spark (128GB) | NVIDIA AI box | 96 | 90 | Qwen3.8 27B | $6,950 (2026-10-15) |
| NVIDIA Jetson AGX Thor Developer Kit | Dev kit | 96 | 90 | Qwen3.8 27B | $5,499 (2026-10-15) |
| Framework Desktop (Ryzen AI Max+ 395) | AMD mini PC | 96 | 90 | Qwen3.8 27B | $1,959 (2026-10-15) |
| GMKtec EVO-X2 (Ryzen AI Max+ 395) | AMD mini PC | 96 | 90 | Qwen3.8 27B | $2,200 (2026-10-15) |
| Minisforum MS-S1 MAX (Ryzen AI Max+ 395) | AMD mini PC | 96 | 90 | Qwen3.8 27B | $2,599 (2026-10-15) |
| Bosgame M5 (Ryzen AI Max+ 395) | AMD mini PC | 96 | 90 | Qwen3.8 27B | $3,599 (2026-10-15) |
| Beelink GTR9 Pro (Ryzen AI Max+ 395) | AMD mini PC | 96 | 90 | Qwen3.8 27B | $4,349 (2026-10-15) |
| ASUS ProArt GR1X (NVIDIA RTX Spark) | NVIDIA AI box | 96 | 90 | Qwen3.8 27B | unknown |
| iBUYPOWER Pro AMD AI Halo Mini PC (Ryzen AI Max+ PRO 495) | AMD mini PC | 96 | 90 | Qwen3.8 27B | $3,999 (2026-10-16) |
| Microsoft Surface RTX Spark Dev Box (128GB) | NVIDIA AI box | 96 | 90 | Qwen3.8 27B | $6,000 (2026-10-16) |
| Sipeed MaixStation | Dev kit | 96 | 90 | Qwen3.8 27B | unknown |

## Questions

**What is the best local LLM for 128 GB of RAM?**
Qwen3.8 27B by Alibaba Qwen. It scores 33.7 on the Artificial Analysis Intelligence Index and needs about 22.7 GB at 4-bit. That is about 59% of Claude Opus 5.5 (57.6), the top paid model. Data as of 2026-10-15.

**How many local models fit in 128 GB?**
90 of the 124 text models in our data fit. We assume about 96 GB is usable for the model (75% of 128 GB), and a model needs its 4-bit file size × 1.15 + 2 GB.

**What is the biggest model that runs in 128 GB?**
Mistral Medium 3.5 128B, 127.7B parameters, needs about 94 GB at 4-bit. Bigger is not always smarter: Qwen3.8 27B scores 33.7; Mistral Medium 3.5 128B scores 14.2.

**What is the best coding model for 128 GB?**
Qwen3.8 27B (needs 22.7 GB, Terminal-Bench 2.1 79.8%). Run it in a coding app like OpenCode or Cline. See https://insidethemachine.ai/best/local-coding-model.

**Does 128 GB of VRAM work the same as 128 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 128 GB card fits a little more than 128 GB of Mac memory. Models that spill out of VRAM into normal RAM run much slower.

More: https://insidethemachine.ai/models?tier=128 · https://insidethemachine.ai/machines?tier=128 · https://insidethemachine.ai/best