MacBook Pro M4 Max 96GBBudget pick
96 GB Unified (+48)
D
Adds memory headroom for longer context windows and future model growth.7 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
SmolVLM 500M Instruct needs ~6.2 GB VRAM. NVIDIA L40 48GB has 48.0 GB. With Q6_K quantization, expect ~8 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
8.0 tok/s
TTFT
24200 ms
Safe context
6.7M
Memory
6.2 GB / 48.0 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 | D | Runs well | 8.0 tok/s | 13200 ms | 3.4M |
| Coding | D | Runs well | 8.0 tok/s | 24200 ms | 6.7M |
| Agentic Coding | D | Runs well | 8.0 tok/s | 35200 ms | 11.4M |
| Reasoning | D | Runs well | 8.0 tok/s | 28600 ms | 6.7M |
| RAG | D | Runs well | 8.0 tok/s | 44000 ms | 11.4M |
Inference speed
Estimated decode speed (tokens/sec) for SmolVLM 500M Instruct at Q6_K across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~10 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 | Q6_K | 9.5 | Fits |
| RTX 4090 24GB | 24 GB | Q6_K | 8.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q6_K | 7.0 | Fits |
| RTX 3090 24GB | 24 GB | Q6_K | 7.0 | Fits |
| RTX 4070 12GB | 12 GB | Q6_K | 7.0 | Fits |
| RTX 3060 12GB | 12 GB | Q6_K | 7.0 | Fits |
| RTX 4060 8GB | 8 GB | Q6_K | 7.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q6_K | 7.0 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q6_K | 7.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q6_K | 7.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q6_K | 7.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q6_K | 7.0 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q6_K | 7.0 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q6_K | 7.0 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q6_K | 7.0 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q6_K | 7.0 | Fits |
Estimates for single-stream decoding at Q6_K; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How SmolVLM 500M Instruct (0.5B params) fits at each quantization level on NVIDIA L40 48GB (48.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.2 GB | Low | C42 |
Q3_K_S | 3 | 0.2 GB | Low | C42 |
NVFP4 | 4 | 0.3 GB | Medium | C42 |
Q4_K_M | 4 | 0.3 GB | Medium | C42 |
Q5_K_M | 5 | 0.4 GB | High | C42 |
Q6_K | 6 | 0.4 GB | High | C42 |
Q8_0 | 8 | 0.5 GB | Very High | C42 |
F16Best for your GPU | 16 | 1.0 GB | Maximum | C42 |
Copy-paste commands to run SmolVLM 500M Instruct on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "ggml-org/SmolVLM-500M-Instruct-GGUF" \
--hf-file "SmolVLM-500M-Instruct-GGUF-Q6_K.gguf" \
-c 4096 -ngl 99Upgrade options
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Yes, NVIDIA L40 48GB can run SmolVLM 500M Instruct with a D grade (Runs well). Expected decode speed: 8.0 tok/s.
SmolVLM 500M Instruct (0.5B parameters) requires approximately 6.2 GB of memory with Q6_K quantization.
The recommended quantization for SmolVLM 500M Instruct is Q6_K, which balances quality and memory efficiency.
On NVIDIA L40 48GB, SmolVLM 500M Instruct achieves approximately 8.0 tokens per second decode speed with a time-to-first-token of 24200ms using Q6_K quantization.
For coding workloads, SmolVLM 500M Instruct on NVIDIA L40 48GB receives a D grade with 8.0 tok/s and 6.7M context.
On NVIDIA L40 48GB, SmolVLM 500M Instruct can safely use up to 6.7M 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-ggml-org--smolvlm-500m-instruct-gguf-on-l40-48gb" 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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