Inside The Machine
16 GB · 38 models fit · data 2026-10-15

Best local LLM for 16 GB.

K2 Horizon 7B is the smartest model that fits in 16 GB. It needs about 8.6 GB and is about 36% as smart as Claude Opus 5.5.

Top picks for 16 GB, about 12 GB usable. AA = Artificial Analysis Intelligence Index; Claude Opus 5.5 = 57.6.
ModelMakerScore (AA)Needs 4-bitNeeds 8-bitRun it
K2 Horizon 7BIFM (MBZUAI)20.68.6 GB13 GBMLX only (Mac)
Gemma 4 12BGoogle14.211.2 GB16.9 GBollama run gemma4:12b
Qwen3.5 9BAlibaba Qwen13.39.6 GB13.5 GBollama run qwen3.5:9b
Qwen3.5 4BAlibaba Qwen13.15.8 GB8 GBollama run qwen3.5:4b
MiniCPM5 2BOpenBMB12.53.9 GB5.1 GBMLX only (Mac)
Granite 4.2 8BIBM11.18.1 GB12.7 GBollama run granite4.2:8b
By job

Pick by what you do.

CodingOrnith 1.5 9B

Needs 8.8 GB.

ollama run ornith-1.5:9b
Agents (uses tools)Mellum2.1 12B-A2.5B Thinking

Needs 11.3 GB.

—
Reads imagesGemma 4 12B

Needs 11.2 GB.

ollama run gemma4:12b
New at 16 GB

What 16 GB adds over 8 GB.

ModelNeedsScore (AA)
K2 Horizon 7B8.6 GB20.6
Gemma 4 12B11.2 GB14.2
Qwen3.5 9B9.6 GB13.3
Granite 4.2 8B8.1 GB11.1
Gemma 4 E4B9.6 GB8.9
Machines

Computers and cards with 16 GB.

Graphics cards count VRAM, so they often fit a bit more.

Prices are approximate and dated. 2026 prices are inflated by the memory-chip shortage.
MachineTypeUsableModels that fitBest fitFrom (approx)
MacBook Air 13"/15" (M5)Mac laptop12 GB38K2 Horizon 7B$1,299 · 2026-10-15
MacBook Pro 14" (M5)Mac laptop12 GB38K2 Horizon 7B$1,999 · 2026-10-15
Mac mini (M6)Mac desktop12 GB38K2 Horizon 7B$899 · 2026-10-15
GeForce RTX 5080NVIDIA graphics card15 GB44K2 Horizon 7B$1,550 · 2026-10-15
GeForce RTX 5070 TiNVIDIA graphics card15 GB44K2 Horizon 7B$1,130 · 2026-10-15
GeForce RTX 5060 Ti 16GBNVIDIA graphics card15 GB44K2 Horizon 7B$740 · 2026-10-15
GeForce RTX 4080 SuperNVIDIA graphics card15 GB44K2 Horizon 7Bunknown
GeForce RTX 4070 Ti SuperNVIDIA graphics card15 GB44K2 Horizon 7Bunknown
GeForce RTX 4060 Ti 16GBNVIDIA graphics card15 GB44K2 Horizon 7Bunknown
Radeon RX 9070 XTAMD graphics card15 GB44K2 Horizon 7B$760 · 2026-10-15
Raspberry Pi 5 (16GB), CPU onlySingle-board computer12 GB38K2 Horizon 7B$305 · 2026-10-12
Questions

Short answers.

What is the best local LLM for 16 GB of RAM?
K2 Horizon 7B by IFM (MBZUAI). It scores 20.6 on the Artificial Analysis Intelligence Index and needs about 8.6 GB at 4-bit. That is about 36% of Claude Opus 5.5 (57.6), the top paid model. Data as of 2026-10-15.
How many local models fit in 16 GB?
38 of the 124 text models in our data fit. We assume about 12 GB is usable for the model (75% of 16 GB), and a model needs its 4-bit file size × 1.15 + 2 GB.
What is the biggest model that runs in 16 GB?
Mellum2.1 12B-A2.5B Thinking, 12.2B (2.5B active) parameters, needs about 11.3 GB at 4-bit. Bigger is not always smarter: K2 Horizon 7B scores 20.6; Mellum2.1 12B-A2.5B Thinking has no published score yet.
What is the best coding model for 16 GB?
Ornith 1.5 9B (needs 8.8 GB). Run it in a coding app like OpenCode or Cline. See https://insidethemachine.ai/best/local-coding-model.
Does 16 GB of VRAM work the same as 16 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 16 GB card fits a little more than 16 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.

AGENT VIEW · text/markdown · or send Accept: text/markdown
# Best local LLM for 16GB RAM or VRAM

URL: https://insidethemachine.ai/best/local-llm-for-16gb-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: 12 GB (75% of 16). 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: 38 of 124.

## Top picks

| model | maker | AA index | needs (4-bit) | needs (8-bit) | run |
|---|---|---|---|---|---|
| K2 Horizon 7B | IFM (MBZUAI) | 20.6 | 8.6 GB | 13 GB | MLX only |
| Gemma 4 12B | Google | 14.2 | 11.2 GB | 16.9 GB | `ollama run gemma4:12b` |
| Qwen3.5 9B | Alibaba Qwen | 13.3 | 9.6 GB | 13.5 GB | `ollama run qwen3.5:9b` |
| Qwen3.5 4B | Alibaba Qwen | 13.1 | 5.8 GB | 8 GB | `ollama run qwen3.5:4b` |
| MiniCPM5 2B | OpenBMB | 12.5 | 3.9 GB | 5.1 GB | MLX only |
| Granite 4.2 8B | IBM | 11.1 | 8.1 GB | 12.7 GB | `ollama run granite4.2:8b` |

## By job

- Coding: Ornith 1.5 9B (8.8 GB) `ollama run ornith-1.5:9b`
- Agents (tool use): Mellum2.1 12B-A2.5B Thinking (11.3 GB) 
- Vision (reads images): Gemma 4 12B (11.2 GB)

## New at 16 GB (did not fit in 8 GB)

- K2 Horizon 7B (8.6 GB, AA 20.6)
- Gemma 4 12B (11.2 GB, AA 14.2)
- Qwen3.5 9B (9.6 GB, AA 13.3)
- Granite 4.2 8B (8.1 GB, AA 11.1)
- Gemma 4 E4B (9.6 GB, AA 8.9)

## Machines with 16 GB

| machine | type | usable GB | models that fit | best fit | from (approx) |
|---|---|---|---|---|---|
| MacBook Air 13"/15" (M5) | Mac laptop | 12 | 38 | K2 Horizon 7B | $1,299 (2026-10-15) |
| MacBook Pro 14" (M5) | Mac laptop | 12 | 38 | K2 Horizon 7B | $1,999 (2026-10-15) |
| Mac mini (M6) | Mac desktop | 12 | 38 | K2 Horizon 7B | $899 (2026-10-15) |
| GeForce RTX 5080 | NVIDIA graphics card | 15 | 44 | K2 Horizon 7B | $1,550 (2026-10-15) |
| GeForce RTX 5070 Ti | NVIDIA graphics card | 15 | 44 | K2 Horizon 7B | $1,130 (2026-10-15) |
| GeForce RTX 5060 Ti 16GB | NVIDIA graphics card | 15 | 44 | K2 Horizon 7B | $740 (2026-10-15) |
| GeForce RTX 4080 Super | NVIDIA graphics card | 15 | 44 | K2 Horizon 7B | unknown |
| GeForce RTX 4070 Ti Super | NVIDIA graphics card | 15 | 44 | K2 Horizon 7B | unknown |
| GeForce RTX 4060 Ti 16GB | NVIDIA graphics card | 15 | 44 | K2 Horizon 7B | unknown |
| Radeon RX 9070 XT | AMD graphics card | 15 | 44 | K2 Horizon 7B | $760 (2026-10-15) |
| Raspberry Pi 5 (16GB), CPU only | Single-board computer | 12 | 38 | K2 Horizon 7B | $305 (2026-10-12) |

## Questions

**What is the best local LLM for 16 GB of RAM?**
K2 Horizon 7B by IFM (MBZUAI). It scores 20.6 on the Artificial Analysis Intelligence Index and needs about 8.6 GB at 4-bit. That is about 36% of Claude Opus 5.5 (57.6), the top paid model. Data as of 2026-10-15.

**How many local models fit in 16 GB?**
38 of the 124 text models in our data fit. We assume about 12 GB is usable for the model (75% of 16 GB), and a model needs its 4-bit file size × 1.15 + 2 GB.

**What is the biggest model that runs in 16 GB?**
Mellum2.1 12B-A2.5B Thinking, 12.2B (2.5B active) parameters, needs about 11.3 GB at 4-bit. Bigger is not always smarter: K2 Horizon 7B scores 20.6; Mellum2.1 12B-A2.5B Thinking has no published score yet.

**What is the best coding model for 16 GB?**
Ornith 1.5 9B (needs 8.8 GB). Run it in a coding app like OpenCode or Cline. See https://insidethemachine.ai/best/local-coding-model.

**Does 16 GB of VRAM work the same as 16 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 16 GB card fits a little more than 16 GB of Mac memory. Models that spill out of VRAM into normal RAM run much slower.

More: https://insidethemachine.ai/models?tier=16 · https://insidethemachine.ai/machines?tier=16 · https://insidethemachine.ai/best