RTX 3060 12GBBudget pick12 GB VRAM (+4)
A
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
Samantha 7B needs ~7.9 GB VRAM. RTX 2060 Super 8GB has 8.0 GB. With Q4_K_M quantization, expect ~61 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
65.4 tok/s
TTFT
2960 ms
Safe context
4K
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 | 65.4 tok/s | 1614 ms | 4K |
| Coding | B | Runs with offload | 60.9 tok/s | 3181 ms | 4K |
| Agentic Coding | F | Too heavy | 30.0 tok/s | 9374 ms | 4K |
| Reasoning | B | Runs with offload | 65.4 tok/s | 3498 ms | 4K |
| RAG | F | Too heavy | 30.0 tok/s | 11717 ms | 4K |
How Samantha 7B (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 | B69 |
Q3_K_S | 3 | 3.4 GB | Low | B70 |
NVFP4 | 4 |
Copy-paste commands to run Samantha 7B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "cognitivecomputations/samantha-1.1-llama-7b" \
--hf-file "samantha-1.1-llama-7b-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
RTX 3060 12GBBudget pickAdds memory headroom for longer context windows and future model growth.
~$329 MSRP
RTX 5070 12GBBest valueRaises estimated decode speed by about 63%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
RTX 4070 Super 12GBNVIDIA upgradeRaises estimated decode speed by about 37%.
Adds memory headroom for longer context windows and future model growth.
~$599 MSRP
Yes, RTX 2060 Super 8GB can run Samantha 7B with a B grade (Runs with offload). Expected decode speed: 60.9 tok/s.
Samantha 7B (7B parameters) requires approximately 7.9 GB of memory with Q4_K_M quantization.
The recommended quantization for Samantha 7B is Q4_K_M, which balances quality and memory efficiency.
On RTX 2060 Super 8GB, Samantha 7B achieves approximately 60.9 tokens per second decode speed with a time-to-first-token of 3181ms using Q4_K_M quantization.
For coding workloads, Samantha 7B on RTX 2060 Super 8GB receives a B grade with 60.9 tok/s and 4K context.
On RTX 2060 Super 8GB, Samantha 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/samantha-7b-on-rtx-2060-super-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 |
| B69 |
Q4_K_M | 4 | 4.3 GB | Medium | B69 |
Q5_K_MBest for your GPU | 5 | 5.0 GB | High | B69 |
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.