Samantha 7B needs ~7.9 GB VRAM. RX 580 8GB has 8.0 GB. With Q4_K_M quantization, expect ~28 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
27.7 tok/s
TTFT
6986 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 | 27.7 tok/s | 3810 ms | 4K |
| Coding | B | Runs with offload | 27.7 tok/s | 6986 ms | 4K |
| Agentic Coding | F | Too heavy | 12.7 tok/s | 22126 ms | 4K |
| Reasoning | B | Runs with offload | 27.7 tok/s | 8256 ms | 4K |
| RAG | F | Too heavy | 12.7 tok/s | 27658 ms | 4K |
How Samantha 7B (7B params) fits at each quantization level on RX 580 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
Sube la velocidad estimada de decodificación alrededor de un 52%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$329 MSRP
Sube la velocidad estimada de decodificación alrededor de un 83%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$349 MSRP
Sube la velocidad estimada de decodificación alrededor de un 136%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$449 MSRP
Yes, RX 580 8GB can run Samantha 7B with a B grade (Runs with offload). Expected decode speed: 27.7 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 RX 580 8GB, Samantha 7B achieves approximately 27.7 tokens per second decode speed with a time-to-first-token of 6986ms using Q4_K_M quantization.
For coding workloads, Samantha 7B on RX 580 8GB receives a B grade with 27.7 tok/s and 4K context.
On RX 580 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-rx-580-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: