Samantha 7B needs ~7.9 GB VRAM. RTX 3000 Ada Laptop 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
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.
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 | 4K |
| Coding | B | Runs with offload | 48.7 tok/s | 3976 ms | 4K |
| Agentic Coding | F | Too heavy | 23.4 tok/s | 12014 ms | 4K |
| Reasoning | B | Runs with offload | 48.7 tok/s | 4699 ms | 4K |
| RAG | F | Too heavy | 23.4 tok/s | 15018 ms | 4K |
How Samantha 7B (7B params) fits at each quantization level on RTX 3000 Ada Laptop 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 | 3.9 GB | 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 |
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 99Opciones de mejora
RTX 3060 12GBOpción económicaAñade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$329 MSRP
RTX 5060 Ti 16GBMejor relación calidad-precioSube la velocidad estimada de decodificación alrededor de un 44%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$449 MSRP
RTX 5070 12GBMejora NVIDIASube la velocidad estimada de decodificación alrededor de un 119%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$549 MSRP
Yes, RTX 3000 Ada Laptop 8GB can run Samantha 7B with a B grade (Runs with offload). Expected decode speed: 48.7 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 3000 Ada Laptop 8GB, Samantha 7B 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, Samantha 7B on RTX 3000 Ada Laptop 8GB receives a B grade with 48.7 tok/s and 4K context.
On RTX 3000 Ada Laptop 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.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/samantha-7b-on-rtx-3000-ada-laptop-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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