MacBook Pro M4 Max 48GBコスパ重視
48 GB Unified (+16)546 GB/s (+34)
B
Raises estimated decode speed by about 31%.94.4 tok/sデコード
Raises estimated decode speed by about 31%.
〜$2,499 MSRP
Can Samantha 7B run on Radeon Pro W6800 32GB?
YES — Runs Great
B64Good
Estimated from fit model
Samantha 7B needs ~10.3 GB VRAM. Radeon Pro W6800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~72 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced
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) — 10.3 GB, 72.2 tok/s, Runs well
10.3 GB required32.0 GB available
32% VRAM used
Fit status
Runs well
Decode
72.2 tok/s
TTFT
2682 ms
Safe context
4K
Memory
10.3 GB / 32.0 GB
Memory breakdown
Weights4.3 GB
KV Cache2.0 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsSamantha 7B on Radeon Pro W6800 32GB
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: 72.2 tok/s decode · 2.7s TTFT (warm) · 181 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 72.2 tok/s | 1463 ms | 4K |
| Coding | B | Runs well | 72.2 tok/s | 2682 ms | 4K |
| Agentic Coding | B | Runs well | 72.2 tok/s | 3901 ms | 4K |
| Reasoning | B | Runs well | 72.2 tok/s | 3170 ms | 4K |
| RAG | B | Runs well | 72.2 tok/s | 4876 ms | 4K |
Inference speed
Samantha 7B inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for Samantha 7B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 98.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 98.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 95.2 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 94.4 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 94.4 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 60.4 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 59.8 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 55.4 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 48.7 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 46.0 | Offloads |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
Quantization options
How Samantha 7B (7B params) fits at each quantization level on Radeon Pro W6800 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B59 |
Q3_K_S | 3 | 3.4 GB | Low | B59 |
NVFP4 | 4 | 3.9 GB | Medium | B59 |
Q4_K_M | 4 | 4.3 GB | Medium | B60 |
Q5_K_M | 5 | 5.0 GB | High | B60 |
Q6_K | 6 | 5.7 GB | High | B60 |
Q8_0 | 8 | 7.5 GB | Very High | B61 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | B64 |
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 99アップグレードオプション
Samantha 7Bを快適に動かすハードウェア
Frequently asked questions
Can Radeon Pro W6800 32GB run Samantha 7B?
Yes, Radeon Pro W6800 32GB can run Samantha 7B with a B grade (Runs well). Expected decode speed: 72.2 tok/s.
How much VRAM does Samantha 7B need?
Samantha 7B (7B parameters) requires approximately 10.3 GB of memory with Q4_K_M quantization.
What is the best quantization for Samantha 7B?
The recommended quantization for Samantha 7B is Q4_K_M, which balances quality and memory efficiency.
What speed will Samantha 7B run at on Radeon Pro W6800 32GB?
On Radeon Pro W6800 32GB, Samantha 7B achieves approximately 72.2 tokens per second decode speed with a time-to-first-token of 2682ms using Q4_K_M quantization.
Can Radeon Pro W6800 32GB run Samantha 7B for coding?
For coding workloads, Samantha 7B on Radeon Pro W6800 32GB receives a B grade with 72.2 tok/s and 4K context.
What context window can Samantha 7B use on Radeon Pro W6800 32GB?
On Radeon Pro W6800 32GB, 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.
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