12 GB VRAM (+4)360 GB/s (+136)
B
Raises estimated decode speed by about 60%.50.2 tok/s decode
Raises estimated decode speed by about 60%.
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Can Mistral 7B Instruct v0.3 run on RTX 3050 8GB?
YES — With Offload
B63Good
○Estimated from fit model
Mistral 7B Instruct v0.3 needs ~7.9 GB VRAM. RTX 3050 8GB has 8.0 GB. With Q4_K_M quantization, expect ~31 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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, 31.3 tok/s, Runs with offload
7.9 GB required8.0 GB available
99% VRAM used
Fit status
Runs with offload
Decode
31.3 tok/s
TTFT
6192 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 3050 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: 31.3 tok/s decode · 6.2s TTFT (warm) · 78 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.
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 | 31.3 tok/s | 3378 ms | 8K |
| Coding | B | Runs with offload | 31.3 tok/s | 6192 ms | 8K |
| Agentic Coding | F | Too heavy | 15.0 tok/s | 18712 ms | 8K |
| Reasoning | B | Runs with offload | 31.3 tok/s | 7318 ms | 8K |
| RAG | F | Too heavy | 15.0 tok/s | 23391 ms | 8K |
Quantization options
How Mistral 7B Instruct v0.3 (7B params) fits at each quantization level on RTX 3050 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
16 GB VRAM (+8)448 GB/s (+224)
B
Raises estimated decode speed by about 123%.69.9 tok/s decode
Raises estimated decode speed by about 123%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
16 GB VRAM (+8)288 GB/s (+64)
B
Raises estimated decode speed by about 56%.48.7 tok/s decode
Raises estimated decode speed by about 56%.
Adds memory headroom for longer context windows and future model growth.
~$499 MSRP