MacBook Pro M4 Max 48GBGünstige Wahl
48 GB Unified (+16)546 GB/s (+34)
C
Raises estimated decode speed by about 51%.35.6 tok/s Dekodierung
Raises estimated decode speed by about 51%.
ca. $2,499 MSRP
Can internlm2 math plus 20b i1 run on Radeon Pro W6800 32GB?
YES — Runs Great
C49Usable
Estimated from fit model
internlm2 math plus 20b i1 needs ~18.6 GB VRAM. Radeon Pro W6800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~24 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 18.6 GB, 23.5 tok/s, Runs well
18.6 GB required32.0 GB available
58% VRAM used
Fit status
Runs well
Decode
23.5 tok/s
TTFT
8238 ms
Safe context
107K
Memory
18.6 GB / 32.0 GB
Memory breakdown
Weights12.2 GB
KV Cache2.3 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsinternlm2 math plus 20b i1 on Radeon Pro W6800 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: 23.5 tok/s decode · 8.2s TTFT (warm) · 59 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 23.5 tok/s | 4493 ms | 107K |
| Coding | C | Runs well | 23.5 tok/s | 8238 ms | 107K |
| Agentic Coding | C | Runs well | 23.5 tok/s | 11982 ms | 107K |
| Reasoning | C | Runs well | 23.5 tok/s | 9735 ms | 107K |
| RAG | C | Runs well | 23.5 tok/s | 14978 ms | 107K |
Quantization options
How internlm2 math plus 20b i1 (20B params) fits at each quantization level on Radeon Pro W6800 32GB (32.0 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 | C46 |
Q4_K_M | 4 | 12.2 GB | Medium | C47 |
Q5_K_M | 5 | 14.4 GB | High | C48 |
Q6_K | 6 | 16.4 GB | High | C49 |
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 startUpgrade-Optionen
Hardware, die internlm2 math plus 20b i1 gut ausführt
Frequently asked questions
Can Radeon Pro W6800 32GB run internlm2 math plus 20b i1?
Yes, Radeon Pro W6800 32GB can run internlm2 math plus 20b i1 with a C grade (Runs well). Expected decode speed: 23.5 tok/s.
How much VRAM does internlm2 math plus 20b i1 need?
internlm2 math plus 20b i1 (20B parameters) requires approximately 18.6 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 Radeon Pro W6800 32GB?
On Radeon Pro W6800 32GB, internlm2 math plus 20b i1 achieves approximately 23.5 tokens per second decode speed with a time-to-first-token of 8238ms using Q4_K_M quantization.
Can Radeon Pro W6800 32GB run internlm2 math plus 20b i1 for coding?
For coding workloads, internlm2 math plus 20b i1 on Radeon Pro W6800 32GB receives a C grade with 23.5 tok/s and 107K context.
What context window can internlm2 math plus 20b i1 use on Radeon Pro W6800 32GB?
On Radeon Pro W6800 32GB, internlm2 math plus 20b i1 can safely use up to 107K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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