SmolVLM 500M Instruct needs ~2.5 GB VRAM. RTX 2070 Super 8GB has 8.0 GB. With Q6_K quantization, expect ~7 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
7.0 tok/s
TTFT
27657 ms
Safe context
894K
Memory
2.5 GB / 8.0 GB
This model fits, but memory bandwidth is the part holding decode speed back.
Throughput will feel slow
Estimated decode speed is only 7.0 tok/s, so this is more of a technical fit than a comfortable daily-driver setup.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 7.0 tok/s | 15086 ms | 447K |
| Coding | C | Runs well | 7.0 tok/s | 27657 ms | 894K |
| Agentic Coding | C | Runs well | 7.0 tok/s | 40229 ms | 1.5M |
| Reasoning | C | Runs well | 7.0 tok/s | 32686 ms | 894K |
| RAG | C | Runs well | 7.0 tok/s | 50286 ms | 1.5M |
How SmolVLM 500M Instruct (0.5B params) fits at each quantization level on RTX 2070 Super 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.2 GB | Low | C49 |
Q3_K_S | 3 | 0.2 GB | Low | C49 |
NVFP4 | 4 |
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 99Yes, RTX 2070 Super 8GB can run SmolVLM 500M Instruct with a C grade (Runs well). Expected decode speed: 7.0 tok/s.
SmolVLM 500M Instruct (0.5B parameters) requires approximately 2.5 GB of memory with Q6_K quantization.
The recommended quantization for SmolVLM 500M Instruct is Q6_K, which balances quality and memory efficiency.
On RTX 2070 Super 8GB, SmolVLM 500M Instruct achieves approximately 7.0 tokens per second decode speed with a time-to-first-token of 27657ms using Q6_K quantization.
For coding workloads, SmolVLM 500M Instruct on RTX 2070 Super 8GB receives a C grade with 7.0 tok/s and 894K context.
On RTX 2070 Super 8GB, SmolVLM 500M Instruct can safely use up to 894K 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-rtx-2070-super-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| C49 |
Q4_K_M | 4 | 0.3 GB | Medium | C49 |
Q5_K_M | 5 | 0.4 GB | High | C49 |
Q6_K | 6 | 0.4 GB | High | C49 |
Q8_0 | 8 | 0.5 GB | Very High | C49 |
F16Best for your GPU | 16 | 1.0 GB | Maximum | C50 |
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.