MacBook Pro M3 Max 128GBBudget pick
128 GB Unified (+48)
C
This setup is broadly balanced for this model.42 tok/s decode
~$2,499 MSRP
SmolLM3 3B needs ~13.0 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~42 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
42.0 tok/s
TTFT
4610 ms
Safe context
128K
Memory
13.0 GB / 80.0 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 42.0 tok/s | 2514 ms | 128K |
| Coding | C | Runs well | 42.0 tok/s | 4610 ms | 128K |
| Agentic Coding | C | Runs well | 42.0 tok/s | 6705 ms | 128K |
| Reasoning | C | Runs well | 42.0 tok/s | 5448 ms | 128K |
| RAG | C | Runs well | 42.0 tok/s | 8381 ms | 128K |
How SmolLM3 3B (3B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.2 GB | Low | C47 |
Q3_K_S | 3 | 1.5 GB | Low | C47 |
NVFP4 | 4 | 1.7 GB | Medium | C47 |
Q4_K_M | 4 | 1.8 GB | Medium | C47 |
Q5_K_M | 5 | 2.2 GB | High | C47 |
Q6_K | 6 | 2.5 GB | High | C47 |
Q8_0 | 8 | 3.2 GB | Very High | C47 |
F16Best for your GPU | 16 | 6.1 GB | Maximum | C48 |
Copy-paste commands to run SmolLM3 3B on your machine.
Run
lms load SmolLM3-3B && lms server startUpgrade options
~$2,499 MSRP
~$3,999 MSRP
NVIDIA DGX Spark 128GBNVIDIA upgradeAdds memory headroom for longer context windows and future model growth.
Yes, NVIDIA A100 80GB can run SmolLM3 3B with a C grade (Runs well). Expected decode speed: 42.0 tok/s.
SmolLM3 3B (3B parameters) requires approximately 13.0 GB of memory with Q4_K_M quantization.
The recommended quantization for SmolLM3 3B is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA A100 80GB, SmolLM3 3B achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q4_K_M quantization.
For coding workloads, SmolLM3 3B on NVIDIA A100 80GB receives a C grade with 42.0 tok/s and 128K context.
On NVIDIA A100 80GB, SmolLM3 3B can safely use up to 128K tokens of context. The model's official context limit is 128K, 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/smollm3-3b-on-a100-80gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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