What can MacBook Pro 16" M4 Max run for chat?

Build: MacBook Pro 16" M4 Max + — + 128 GB RAM (macos)

Memory: 128 GB unified memory
Runner: MLX-LM (Apple Metal)
AnyChatCodingAgentsReasoningVisionLong contextCreative

Runs comfortably
262 models

Ranked by fit for chat use case + predicted speed. Click a row for VRAM breakdown.

#1Qwen 3 0.6B
0.6B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 1.1 GBHeadroom: 118.9 GB
829
tok/s
Estimated
Weights
0.30 GB
KV cache
0.30 GB
Activations
0.02 GB
Runtime
0.50 GB
Model details →
#2Qwen 3 1.7B
1.7B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 2.3 GBHeadroom: 117.7 GB
293
tok/s
Estimated
Weights
0.90 GB
KV cache
0.85 GB
Activations
0.05 GB
Runtime
0.50 GB
Model details →
#3TinyLlama 1.1B Chat v1.0
1.1B
llama
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 1.3 GBHeadroom: 118.7 GB
452
tok/s
Estimated
Weights
0.60 GB
KV cache
0.14 GB
Activations
0.03 GB
Runtime
0.50 GB
Model details →
#4Gemma 2 2B Instruct
2B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 2.7 GBHeadroom: 117.3 GB
249
tok/s
Estimated
Weights
1.10 GB
KV cache
1.00 GB
Activations
0.06 GB
Runtime
0.50 GB
Model details →
#5DeepSeek V2 Lite Chat
15.7B
deepseek
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 17.4 GBHeadroom: 102.6 GB
207
tok/s
Estimated
Weights
8.60 GB
KV cache
7.85 GB
Activations
0.44 GB
Runtime
0.50 GB
Model details →
#6Kumru 2B
2.4B
mistral
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 3.3 GBHeadroom: 116.7 GB
ollama run alibayram/kumru:latest
207
tok/s
Estimated
Weights
1.50 GB
KV cache
1.20 GB
Activations
0.08 GB
Runtime
0.50 GB
Model details →
#7TinyLlama 1.1B Chat v0.3 AWQ
1.1B
other
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 1.3 GBHeadroom: 118.7 GB
452
tok/s
Estimated
Weights
0.60 GB
KV cache
0.14 GB
Activations
0.03 GB
Runtime
0.50 GB
Model details →
#8TinyLlama 1.1B Chat v0.3 GPTQ
1.1B
other
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 1.3 GBHeadroom: 118.7 GB
452
tok/s
Estimated
Weights
0.60 GB
KV cache
0.14 GB
Activations
0.03 GB
Runtime
0.50 GB
Model details →
#9EXAONE 3.5 2.4B Instruct
2.4B
exaone
Quant: Q4_K_MContext: 8,192VRAM: 3.1 GBHeadroom: 116.9 GB
207
tok/s
Estimated
Weights
1.30 GB
KV cache
1.20 GB
Activations
0.07 GB
Runtime
0.50 GB
Model details →
#10Falcon 3 3B Instruct
3B
falcon
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 3.8 GBHeadroom: 116.2 GB
166
tok/s
Estimated
Weights
1.70 GB
KV cache
1.50 GB
Activations
0.09 GB
Runtime
0.50 GB
Model details →
#11PhoGPT 4B Chat
3.7B
other
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 4.5 GBHeadroom: 115.5 GB
134
tok/s
Estimated
Weights
2.00 GB
KV cache
1.85 GB
Activations
0.11 GB
Runtime
0.50 GB
Model details →
#12Salamandra 2B Instruct
2B
llama
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 2.7 GBHeadroom: 117.3 GB
249
tok/s
Estimated
Weights
1.10 GB
KV cache
1.00 GB
Activations
0.06 GB
Runtime
0.50 GB
Model details →

What if you upgraded?

Hypothetical scenarios. We re-ran the compatibility engine for each.

Move up an Apple memory tier

~$200–400 over base

On Apple Silicon, more unified memory is the only path forward — VRAM and system RAM are the same pool.

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Won't run
top 5 popular models

Need more memory than you have. Shown for orientation.

DeepSeek V4 Pro (1.6T MoE)
1600B
deepseek
Commercial OK

Needs ~1024 GB unified memory minimum at smallest quant; you have 120 GB available after OS overhead.

—
Qwen 3.5 235B-A17B (MoE)
397B
qwen
Commercial OK

Needs ~256 GB unified memory minimum at smallest quant; you have 120 GB available after OS overhead.

—
Qwen 3 235B-A22B
235B
qwen
Commercial OK

Needs ~160 GB unified memory minimum at smallest quant; you have 120 GB available after OS overhead.

—
DeepSeek R1 (671B reasoning)
671B
deepseek
Commercial OK

Needs ~420 GB unified memory minimum at smallest quant; you have 120 GB available after OS overhead.

—
DeepSeek V4 Flash (284B MoE)
284B
deepseek
Commercial OK

Needs ~192 GB unified memory minimum at smallest quant; you have 120 GB available after OS overhead.

—

How to read these numbers

Measured here
Measured here - RunLocalAI ran this exact combo on owner hardware with public evidence.

Source-backed
Source-backed / community - a reproduced public source supports the speed, but it is not labeled as owner-measured.

Extrapolated
Extrapolated - predicted from a measured benchmark on similar-bandwidth hardware.

Estimated
Estimated - formula based on VRAM bandwidth and model architecture; not a benchmark row.

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