LLaVA 1.5 7B needs ~14.9 GB VRAM. RTX 4080 Super 16GB has 16.0 GB. With Q4_K_M quantization, expect ~98 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
Tight fit
Decode
98.0 tok/s
TTFT
1976 ms
Safe context
4K
Memory
14.9 GB / 16.0 GB
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 98.0 tok/s | 1078 ms | 4K |
| Coding | A | Tight fit | 98.0 tok/s | 1976 ms | 4K |
| Agentic Coding | F | Too heavy | 51.4 tok/s | 5478 ms | 4K |
| Reasoning | A | Tight fit | 98.0 tok/s | 2335 ms | 4K |
| RAG | F | Too heavy | 51.4 tok/s | 6847 ms | 4K |
How LLaVA 1.5 7B (7B params) fits at each quantization level on RTX 4080 Super 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B65 |
Q3_K_S | 3 | 3.4 GB | Low | B65 |
NVFP4 | 4 |
Copy-paste commands to run LLaVA 1.5 7B on your machine.
Run
ollama run llavaYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
| 9B | S | 119.6 tok/s | ||
| 14B | S | 77.3 tok/s |
Yes, RTX 4080 Super 16GB can run LLaVA 1.5 7B with a A grade (Tight fit). Expected decode speed: 98.0 tok/s.
LLaVA 1.5 7B (7B parameters) requires approximately 14.9 GB of memory with Q4_K_M quantization.
The recommended quantization for LLaVA 1.5 7B is Q4_K_M, which balances quality and memory efficiency.
On RTX 4080 Super 16GB, LLaVA 1.5 7B achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.
For coding workloads, LLaVA 1.5 7B on RTX 4080 Super 16GB receives a A grade with 98.0 tok/s and 4K context.
On RTX 4080 Super 16GB, LLaVA 1.5 7B can safely use up to 4K tokens of context. The model's official context limit is 4K, 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/llava-1.5-7b-on-rtx-4080-super-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
3.9 GB |
| Medium |
| B66 |
Q4_K_M | 4 | 4.3 GB | Medium | B66 |
Q5_K_M | 5 | 5.0 GB | High | B67 |
Q6_K | 6 | 5.7 GB | High | B67 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | B69 |
F16 | 16 | 14.3 GB | Maximum | F0 |
| 8B | S | 112 tok/s |
| 14.7B | S | 73.2 tok/s |
| 21B | A | 68.2 tok/s |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.