12 GB VRAM (+4)
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
Can Mistral 7B Instruct v0.3 run on RTX 2060 Super 8GB?
YES — With Offload
B65Good
○Estimated from fit model
Mistral 7B Instruct v0.3 needs ~7.9 GB VRAM. RTX 2060 Super 8GB has 8.0 GB. With Q4_K_M quantization, expect ~65 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Balanced
Share:
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 7.9 GB, 65.4 tok/s, Runs with offload
7.9 GB required8.0 GB available
99% VRAM used
Fit status
Runs with offload
Decode
65.4 tok/s
TTFT
2960 ms
Safe context
8K
Memory
7.9 GB / 8.0 GB
Memory breakdown
Weights4.3 GB
KV Cache2.0 GB
Runtime0.9 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsMistral 7B Instruct v0.3 on RTX 2060 Super 8GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.
Given information:
- Speed = 60 mph (miles per hour)
- Time = 2.5 hours
Formula: Distance = Speed x Time
Calculation:
Distance = 60 mph x 2.5 h
= 150 miles
The train travels 150 miles.
Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.
Breakdown:
- First half: 1.25 hours at 60 mph = 75 miles
- Stop: 20 minutes (no distance)
- Second half: 1.25 hours at 60 mph = 75 miles
Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph
The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 65.4 tok/s decode · 3.0s TTFT (warm) · 164 tok/s prefill
What limits this setup
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.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Tight fit | 65.4 tok/s | 1614 ms | 8K |
| Coding | B | Runs with offload | 65.4 tok/s | 2960 ms | 8K |
| Agentic Coding | F | Too heavy | 30.0 tok/s | 9374 ms | 8K |
| Reasoning | B | Runs with offload | 65.4 tok/s | 3498 ms | 8K |
| RAG | F | Too heavy | 30.0 tok/s | 11717 ms | 8K |
Quantization options
How Mistral 7B Instruct v0.3 (7B params) fits at each quantization level on RTX 2060 Super 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 |
Get started
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
Hardware that runs Mistral 7B Instruct v0.3 well
12 GB VRAM (+4)672 GB/s (+224)
B
Raises estimated decode speed by about 63%.106.6 tok/s decode
Raises estimated decode speed by about 63%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
12 GB VRAM (+4)504 GB/s (+56)
B
Raises estimated decode speed by about 37%.89.9 tok/s decode
Raises estimated decode speed by about 37%.
Adds memory headroom for longer context windows and future model growth.
~$599 MSRP