MacBook Pro M4 Pro 24GBBudget pick
24 GB Unified (+8)
B
This setup is broadly balanced for this model.48.7 tok/s decode
~$1,999 MSRP
Can Samantha 7B run on NVIDIA T4 16GB?
YES — Runs Great
B68Good
○Estimated from fit model
Samantha 7B needs ~9.0 GB VRAM. NVIDIA T4 16GB has 16.0 GB. With Q4_K_M quantization, expect ~52 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: 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) — 9.0 GB, 52.4 tok/s, Runs well
9.0 GB required16.0 GB available
56% VRAM used
Fit status
Runs well
Decode
52.4 tok/s
TTFT
3697 ms
Safe context
4K
Memory
9.0 GB / 16.0 GB
Memory breakdown
Weights4.3 GB
KV Cache2.0 GB
Runtime1.2 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsSamantha 7B on NVIDIA T4 16GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 52.4 tok/s decode · 3.7s TTFT (warm) · 131 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
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
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 52.4 tok/s | 2017 ms | 4K |
| Coding | B | Runs well | 52.4 tok/s | 3697 ms | 4K |
| Agentic Coding | A | Runs well | 52.4 tok/s | 5378 ms | 4K |
| Reasoning | B | Runs well | 52.4 tok/s | 4369 ms | 4K |
| RAG | A | Runs well | 52.4 tok/s | 6722 ms | 4K |
Quantization options
How Samantha 7B (7B params) fits at each quantization level on NVIDIA T4 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B63 |
Q3_K_S | 3 | 3.4 GB | Low | B63 |
NVFP4 | 4 |
Get started
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