RTX 3060 12GBBudget pick12 GB VRAM (+4)360 GB/s (+72)
B
Adds memory headroom for longer context windows and future model growth.50.2 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Mistral 7B Instruct v0.3 needs ~7.9 GB VRAM. RTX 4060 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~49 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 with offload
Decode
48.7 tok/s
TTFT
3976 ms
Safe context
8K
Memory
7.9 GB / 8.0 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 | B | Tight fit | 48.7 tok/s | 2169 ms | 8K |
| Coding | B | Runs with offload | 48.7 tok/s | 3976 ms | 8K |
| Agentic Coding | F | Too heavy | 23.4 tok/s | 12014 ms | 8K |
| Reasoning | B | Runs with offload | 48.7 tok/s | 4699 ms | 8K |
| RAG | F | Too heavy | 23.4 tok/s | 15018 ms | 8K |
How Mistral 7B Instruct v0.3 (7B params) fits at each quantization level on RTX 4060 Ti 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B65 |
Q3_K_S | 3 | 3.4 GB | Low | B66 |
NVFP4 | 4 |
Copy-paste commands to run Mistral 7B Instruct v0.3 on your machine.
Run
lms load Mistral-7B-Instruct-v0.3 && lms server startUpgrade options
RTX 3060 12GBBudget pickAdds memory headroom for longer context windows and future model growth.
~$329 MSRP
RTX 5060 Ti 16GBBest valueRaises estimated decode speed by about 44%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
RTX 5070 12GBNVIDIA upgradeRaises estimated decode speed by about 119%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
Yes, RTX 4060 Ti 8GB can run Mistral 7B Instruct v0.3 with a B grade (Runs with offload). Expected decode speed: 48.7 tok/s.
Mistral 7B Instruct v0.3 (7B parameters) requires approximately 7.9 GB of memory with Q4_K_M quantization.
The recommended quantization for Mistral 7B Instruct v0.3 is Q4_K_M, which balances quality and memory efficiency.
On RTX 4060 Ti 8GB, Mistral 7B Instruct v0.3 achieves approximately 48.7 tokens per second decode speed with a time-to-first-token of 3976ms using Q4_K_M quantization.
For coding workloads, Mistral 7B Instruct v0.3 on RTX 4060 Ti 8GB receives a B grade with 48.7 tok/s and 8K context.
On RTX 4060 Ti 8GB, Mistral 7B Instruct v0.3 can safely use up to 8K tokens of context. The model's official context limit is 8K, 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/mistral-7b-instruct-v0.3-on-rtx-4060-ti-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 |
| B66 |
Q4_K_M | 4 | 4.3 GB | Medium | B65 |
Q5_K_MBest for your GPU | 5 | 5.0 GB | High | B65 |
Q6_K | 6 | 5.7 GB | High | F0 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.