RTX PRO 5000 Blackwell 48GB经济之选1344 GB/s (+944)
C
Raises estimated decode speed by about 370%.92.5 tok/s 解码
Raises estimated decode speed by about 370%.
Adds memory headroom for longer context windows and future model growth.
~$4,999 MSRP
Can internlm2 math plus 20b i1 run on MacBook Pro M3 Max 48GB?
YES — Runs Great
C49Usable
Estimated from fit model
internlm2 math plus 20b i1 needs ~20.6 GB VRAM. MacBook Pro M3 Max 48GB has 34.6 GB. With Q4_K_M quantization, expect ~20 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 20.6 GB, 19.7 tok/s, Runs well
20.6 GB required34.6 GB available
60% VRAM used
Fit status
Runs well
Decode
19.7 tok/s
TTFT
9841 ms
Safe context
111K
Memory
20.6 GB / 34.6 GB
Memory breakdown
Weights12.2 GB
KV Cache2.3 GB
Runtime0.9 GB
Headroom5.2 GB
See how fast it feels
See how fast it feelsinternlm2 math plus 20b i1 on MacBook Pro M3 Max 48GB
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: 19.7 tok/s decode · 9.8s TTFT (warm) · 49 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 | 19.7 tok/s | 5368 ms | 111K |
| Coding | C | Runs well | 19.7 tok/s | 9841 ms | 111K |
| Agentic Coding | C | Runs well | 19.7 tok/s | 14315 ms | 111K |
| Reasoning | C | Runs well | 19.7 tok/s | 11631 ms | 111K |
| RAG | C | Runs well | 19.7 tok/s | 17893 ms | 111K |
Quantization options
How internlm2 math plus 20b i1 (20B params) fits at each quantization level on MacBook Pro M3 Max 48GB (34.6 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.8 GB | Low | C44 |
Q3_K_S | 3 | 9.8 GB | Low | C45 |
NVFP4 | 4 | 11.2 GB | Medium | C45 |
Q4_K_M | 4 | 12.2 GB | Medium | C46 |
Q5_K_M | 5 | 14.4 GB | High | C47 |
Q6_K | 6 | 16.4 GB | High | C48 |
Q8_0Best for your GPU | 8 | 21.4 GB | Very High | C48 |
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 start升级选项
能流畅运行 internlm2 math plus 20b i1 的硬件
Frequently asked questions
Can MacBook Pro M3 Max 48GB run internlm2 math plus 20b i1?
Yes, MacBook Pro M3 Max 48GB can run internlm2 math plus 20b i1 with a C grade (Runs well). Expected decode speed: 19.7 tok/s.
How much VRAM does internlm2 math plus 20b i1 need?
internlm2 math plus 20b i1 (20B parameters) requires approximately 20.6 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 M3 Max 48GB?
On MacBook Pro M3 Max 48GB, internlm2 math plus 20b i1 achieves approximately 19.7 tokens per second decode speed with a time-to-first-token of 9841ms using Q4_K_M quantization.
Can MacBook Pro M3 Max 48GB run internlm2 math plus 20b i1 for coding?
For coding workloads, internlm2 math plus 20b i1 on MacBook Pro M3 Max 48GB receives a C grade with 19.7 tok/s and 111K context.
What context window can internlm2 math plus 20b i1 use on MacBook Pro M3 Max 48GB?
On MacBook Pro M3 Max 48GB, internlm2 math plus 20b i1 can safely use up to 111K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M3 Max 48GB as fast as VRAM for internlm2 math plus 20b i1?
Not always. MacBook Pro M3 Max 48GB 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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