NVIDIA A100 80GBBudget pick2039 GB/s (+1220)
C
Raises estimated decode speed by about 208%.140.4 tok/s decode
Raises estimated decode speed by about 208%.
~$15,000 MSRP
internlm2 5 20b chat needs ~25.8 GB VRAM. Mac Studio M3 Ultra 96GB has 69.1 GB. With Q4_K_M quantization, expect ~46 tok/s.
Operating mode
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
Fit status
Runs well
Decode
45.6 tok/s
TTFT
4241 ms
Safe context
312K
Memory
25.8 GB / 69.1 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 45.6 tok/s | 2313 ms | 312K |
| Coding | C | Runs well | 45.6 tok/s | 4241 ms | 312K |
| Agentic Coding | C | Runs well | 45.6 tok/s | 6169 ms | 312K |
| Reasoning | C | Runs well | 45.6 tok/s | 5012 ms | 312K |
| RAG | C | Runs well | 45.6 tok/s | 7711 ms | 312K |
How internlm2 5 20b chat (20B params) fits at each quantization level on Mac Studio M3 Ultra 96GB (69.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.8 GB | Low | C40 |
Q3_K_S | 3 | 9.8 GB | Low | C40 |
NVFP4 | 4 |
Copy-paste commands to run internlm2 5 20b chat on your machine.
Run
lms load hf-bartowski--internlm2-5-20b-chat-gguf && lms server startUpgrade options
NVIDIA A100 80GBBudget pickRaises estimated decode speed by about 208%.
~$15,000 MSRP
NVIDIA A800 80GBBest valueRaises estimated decode speed by about 171%.
~$15,000 MSRP
Yes, Mac Studio M3 Ultra 96GB can run internlm2 5 20b chat with a C grade (Runs well). Expected decode speed: 45.6 tok/s.
internlm2 5 20b chat (20B parameters) requires approximately 25.8 GB of memory with Q4_K_M quantization.
The recommended quantization for internlm2 5 20b chat is Q4_K_M, which balances quality and memory efficiency.
On Mac Studio M3 Ultra 96GB, internlm2 5 20b chat achieves approximately 45.6 tokens per second decode speed with a time-to-first-token of 4241ms using Q4_K_M quantization.
For coding workloads, internlm2 5 20b chat on Mac Studio M3 Ultra 96GB receives a C grade with 45.6 tok/s and 312K context.
On Mac Studio M3 Ultra 96GB, internlm2 5 20b chat can safely use up to 312K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-bartowski--internlm2-5-20b-chat-gguf-on-m3-ultra-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
11.2 GB |
| Medium |
| C41 |
Q4_K_M | 4 | 12.2 GB | Medium | C41 |
Q5_K_M | 5 | 14.4 GB | High | C41 |
Q6_K | 6 | 16.4 GB | High | C42 |
Q8_0 | 8 | 21.4 GB | Very High | C43 |
F16Best for your GPU | 16 | 41.0 GB | Maximum | C47 |
Not always. Mac Studio M3 Ultra 96GB 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.