MacBook Pro M3 Pro 36GB经济之选
36 GB Unified (+12)
C
Adds memory headroom for longer context windows and future model growth.9 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Can internlm2 5 20b chat run on MacBook Pro M4 Pro 24GB?
YES — With Offload
C49Usable
Estimated — low-sample bucket· few comparable runs
internlm2 5 20b chat needs ~18.0 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~21 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Balanced
Operating mode
Choose the run profile you care about
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Q4_K_M (Medium quality) — 18.0 GB, 20.6 tok/s, Runs with offload (needs ~0.5 GB host RAM)
18.0 GB required17.3 GB available
104% VRAM needed
0.7 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.5 GB host RAM)
Decode
20.6 tok/s
TTFT
9385 ms
Safe context
11K
Memory
18.0 GB / 17.3 GB
Memory breakdown
Weights12.2 GB
KV Cache2.3 GB
Runtime0.9 GB
Headroom2.6 GB
See how fast it feels
See how fast it feelsinternlm2 5 20b chat on MacBook Pro M4 Pro 24GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 20.6 tok/s decode · 9.4s TTFT (warm) · 52 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload | 22.4 tok/s | 4714 ms | 11K |
| Coding | C | Runs with offload (needs ~0.5 GB host RAM) | 20.6 tok/s | 9385 ms | 11K |
| Agentic Coding | D | Very compromised (needs ~1.9 GB host RAM) | 17.4 tok/s | 16152 ms | 11K |
| Reasoning | C | Runs with offload (needs ~0.5 GB host RAM) | 20.6 tok/s | 11091 ms | 11K |
| RAG | D | Very compromised (needs ~1.9 GB host RAM) | 17.4 tok/s | 20190 ms | 11K |
Inference speed
internlm2 5 20b chat inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for internlm2 5 20b chat at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 98.4 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 62.8 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 56.7 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 53.7 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 45.6 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 38.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 36.1 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 35.6 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 35.6 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 31.7 | Heavy offload |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 22.4 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 19.7 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 18.0 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 11.2 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 7.1 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.6 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
Quantization options
How internlm2 5 20b chat (20B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.8 GB | Low | C51 |
Q3_K_S | 3 | 9.8 GB | Low | C51 |
NVFP4 | 4 | 11.2 GB | Medium | C50 |
Q4_K_MBest for your GPU | 4 | 12.2 GB | Medium | C50 |
Q5_K_M | 5 | 14.4 GB | High | F0 |
Q6_K | 6 | 16.4 GB | High | F0 |
Q8_0 | 8 | 21.4 GB | Very High | F0 |
F16 | 16 | 41.0 GB | Maximum | F0 |
Get started
Copy-paste commands to run internlm2 5 20b chat on your machine.
Run
lms load hf-bartowski--internlm2-5-20b-chat-gguf && lms server start升级选项
能流畅运行 internlm2 5 20b chat 的硬件
MacBook Pro M4 Max 36GB最佳性价比
36 GB Unified (+12)410 GB/s (+137)
C
Raises estimated decode speed by about 37%.28.3 tok/s 解码
Raises estimated decode speed by about 37%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
MacBook Pro M4 Max 48GBApple 升级
48 GB Unified (+24)546 GB/s (+273)
C
Raises estimated decode speed by about 73%.35.6 tok/s 解码
Raises estimated decode speed by about 73%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Frequently asked questions
Can MacBook Pro M4 Pro 24GB run internlm2 5 20b chat?
Yes, MacBook Pro M4 Pro 24GB can run internlm2 5 20b chat with a C grade (Runs with offload (needs ~0.5 GB host RAM)). Expected decode speed: 20.6 tok/s.
How much VRAM does internlm2 5 20b chat need?
internlm2 5 20b chat (20B parameters) requires approximately 18.0 GB of memory with Q4_K_M quantization.
What is the best quantization for internlm2 5 20b chat?
The recommended quantization for internlm2 5 20b chat is Q4_K_M, which balances quality and memory efficiency.
What speed will internlm2 5 20b chat run at on MacBook Pro M4 Pro 24GB?
On MacBook Pro M4 Pro 24GB, internlm2 5 20b chat achieves approximately 20.6 tokens per second decode speed with a time-to-first-token of 9385ms using Q4_K_M quantization.
Can MacBook Pro M4 Pro 24GB run internlm2 5 20b chat for coding?
For coding workloads, internlm2 5 20b chat on MacBook Pro M4 Pro 24GB receives a C grade with 20.6 tok/s and 11K context.
What context window can internlm2 5 20b chat use on MacBook Pro M4 Pro 24GB?
On MacBook Pro M4 Pro 24GB, internlm2 5 20b chat can safely use up to 11K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if internlm2 5 20b chat feels slow on MacBook Pro M4 Pro 24GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Is unified memory on MacBook Pro M4 Pro 24GB as fast as VRAM for internlm2 5 20b chat?
Not always. MacBook Pro M4 Pro 24GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
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