MacBook Pro M3 Pro 36GBOpção econômica
36 GB Unified (+4)
C
This setup is broadly balanced for this model.9 tok/s decodificação
~$1,999 MSRP
Can internlm2 math plus 20b i1 run on MacBook Pro M2 Max 32GB?
YES — Tight Fit
C48Usable
Estimated from fit model
internlm2 math plus 20b i1 needs ~18.9 GB VRAM. MacBook Pro M2 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~19 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: 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) — 18.9 GB, 19.0 tok/s, Tight fit
18.9 GB required23.0 GB available
82% VRAM used
Fit status
Tight fit
Decode
19.0 tok/s
TTFT
10181 ms
Safe context
44K
Memory
18.9 GB / 23.0 GB
Memory breakdown
Weights12.2 GB
KV Cache2.3 GB
Runtime0.9 GB
Headroom3.5 GB
See how fast it feels
See how fast it feelsinternlm2 math plus 20b i1 on MacBook Pro M2 Max 32GB
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: 19.0 tok/s decode · 10.2s TTFT (warm) · 48 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
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
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 19.0 tok/s | 5553 ms | 44K |
| Coding | C | Tight fit | 19.0 tok/s | 10181 ms | 44K |
| Agentic Coding | C | Tight fit | 19.0 tok/s | 14808 ms | 44K |
| Reasoning | C | Tight fit | 19.0 tok/s | 12032 ms | 44K |
| RAG | C | Tight fit | 19.0 tok/s | 18510 ms | 44K |
Inference speed
internlm2 math plus 20b i1 inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for internlm2 math plus 20b i1 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 math plus 20b i1 (20B params) fits at each quantization level on MacBook Pro M2 Max 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.8 GB | Low | C47 |
Q3_K_S | 3 | 9.8 GB | Low | C49 |
NVFP4 | 4 | 11.2 GB | Medium | C50 |
Q4_K_M | 4 | 12.2 GB | Medium | C50 |
Q5_K_M | 5 | 14.4 GB | High | C50 |
Q6_KBest for your GPU | 6 | 16.4 GB | High | C49 |
Q8_0 | 8 | 21.4 GB | Very High | F0 |
F16 | 16 | 41.0 GB | Maximum | F0 |
Get started
Copy-paste commands to run internlm2 math plus 20b i1 on your machine.
Run
lms load hf-mradermacher--internlm2-math-plus-20b-i1-gguf && lms server startOpções de upgrade
Hardware que roda bem internlm2 math plus 20b i1
MacBook Pro M4 Max 36GBMelhor custo-benefício
36 GB Unified (+4)410 GB/s (+10)
C
Raises estimated decode speed by about 49%.28.3 tok/s decodificação
Raises estimated decode speed by about 49%.
~$2,499 MSRP
MacBook Pro M4 Max 48GBUpgrade Apple
48 GB Unified (+16)546 GB/s (+146)
C
Raises estimated decode speed by about 87%.35.6 tok/s decodificação
Raises estimated decode speed by about 87%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Frequently asked questions
Can MacBook Pro M2 Max 32GB run internlm2 math plus 20b i1?
Yes, MacBook Pro M2 Max 32GB can run internlm2 math plus 20b i1 with a C grade (Tight fit). Expected decode speed: 19.0 tok/s.
How much VRAM does internlm2 math plus 20b i1 need?
internlm2 math plus 20b i1 (20B parameters) requires approximately 18.9 GB of memory with Q4_K_M quantization.
What is the best quantization for internlm2 math plus 20b i1?
The recommended quantization for internlm2 math plus 20b i1 is Q4_K_M, which balances quality and memory efficiency.
What speed will internlm2 math plus 20b i1 run at on MacBook Pro M2 Max 32GB?
On MacBook Pro M2 Max 32GB, internlm2 math plus 20b i1 achieves approximately 19.0 tokens per second decode speed with a time-to-first-token of 10181ms using Q4_K_M quantization.
Can MacBook Pro M2 Max 32GB run internlm2 math plus 20b i1 for coding?
For coding workloads, internlm2 math plus 20b i1 on MacBook Pro M2 Max 32GB receives a C grade with 19.0 tok/s and 44K context.
What context window can internlm2 math plus 20b i1 use on MacBook Pro M2 Max 32GB?
On MacBook Pro M2 Max 32GB, internlm2 math plus 20b i1 can safely use up to 44K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M2 Max 32GB as fast as VRAM for internlm2 math plus 20b i1?
Not always. MacBook Pro M2 Max 32GB 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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