RTX 3060 12GBBudget pick12 GB VRAM (+4)360 GB/s (+104)
B
Raises estimated decode speed by about 32%.50.2 tok/s decode
Raises estimated decode speed by about 32%.
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Mistral 7B Instruct v0.3 needs ~7.9 GB VRAM. GTX 1070 8GB has 8.0 GB. With Q4_K_M quantization, expect ~38 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
38.0 tok/s
TTFT
5091 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.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
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 | 38.0 tok/s | 2777 ms | 8K |
| Coding | B | Runs with offload | 38.0 tok/s | 5091 ms | 8K |
| Agentic Coding | F | Too heavy | 17.5 tok/s | 16126 ms | 8K |
| Reasoning | B | Runs with offload | 38.0 tok/s | 6017 ms | 8K |
| RAG | F | Too heavy | 17.5 tok/s | 20157 ms | 8K |
How Mistral 7B Instruct v0.3 (7B params) fits at each quantization level on GTX 1070 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 pickRaises estimated decode speed by about 32%.
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
RTX 5060 Ti 16GBBest valueRaises estimated decode speed by about 84%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
RTX 4060 Ti 16GBNVIDIA upgradeRaises estimated decode speed by about 28%.
Adds memory headroom for longer context windows and future model growth.
~$499 MSRP
Yes, GTX 1070 8GB can run Mistral 7B Instruct v0.3 with a B grade (Runs with offload). Expected decode speed: 38.0 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 GTX 1070 8GB, Mistral 7B Instruct v0.3 achieves approximately 38.0 tokens per second decode speed with a time-to-first-token of 5091ms using Q4_K_M quantization.
For coding workloads, Mistral 7B Instruct v0.3 on GTX 1070 8GB receives a B grade with 38.0 tok/s and 8K context.
On GTX 1070 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-gtx-1070-8gb" 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.