MacBook Pro M4 Max 96GBOpção econômica
96 GB Unified (+48)
C
Adds memory headroom for longer context windows and future model growth.94.4 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Can Starling LM 7B run on Quadro RTX 8000 48GB?
YES — Runs Great
C48Usable
Estimated from fit model
Starling LM 7B needs ~12.2 GB VRAM. Quadro RTX 8000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~98 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: 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) — 12.2 GB, 98.0 tok/s, Runs well
12.2 GB required48.0 GB available
25% VRAM used
Fit status
Runs well
Decode
98.0 tok/s
TTFT
1976 ms
Safe context
8K
Memory
12.2 GB / 48.0 GB
Memory breakdown
Weights4.3 GB
KV Cache2.0 GB
Runtime1.2 GB
Headroom4.8 GB
See how fast it feels
See how fast it feelsStarling LM 7B on Quadro RTX 8000 48GB
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: 98.0 tok/s decode · 2.0s TTFT (warm) · 245 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 | C | Runs well | 98.0 tok/s | 1078 ms | 8K |
| Coding | C | Runs well | 98.0 tok/s | 1976 ms | 8K |
| Agentic Coding | C | Runs well | 98.0 tok/s | 2873 ms | 8K |
| Reasoning | C | Runs well | 98.0 tok/s | 2335 ms | 8K |
| RAG | C | Runs well | 98.0 tok/s | 3592 ms | 8K |
Quantization options
How Starling LM 7B (7B params) fits at each quantization level on Quadro RTX 8000 48GB (48.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C42 |
Q3_K_S | 3 | 3.4 GB | Low | C42 |
NVFP4 | 4 | 3.9 GB | Medium | C42 |
Q4_K_M | 4 | 4.3 GB | Medium | C42 |
Q5_K_M | 5 | 5.0 GB | High | C42 |
Q6_K | 6 | 5.7 GB | High | C42 |
Q8_0 | 8 | 7.5 GB | Very High | C42 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C44 |
Get started
Copy-paste commands to run Starling LM 7B on your machine.
Run
ollama run starling-lmOpções de upgrade
Hardware que roda bem Starling LM 7B
Frequently asked questions
Can Quadro RTX 8000 48GB run Starling LM 7B?
Yes, Quadro RTX 8000 48GB can run Starling LM 7B with a C grade (Runs well). Expected decode speed: 98.0 tok/s.
How much VRAM does Starling LM 7B need?
Starling LM 7B (7B parameters) requires approximately 12.2 GB of memory with Q4_K_M quantization.
What is the best quantization for Starling LM 7B?
The recommended quantization for Starling LM 7B is Q4_K_M, which balances quality and memory efficiency.
What speed will Starling LM 7B run at on Quadro RTX 8000 48GB?
On Quadro RTX 8000 48GB, Starling LM 7B achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.
Can Quadro RTX 8000 48GB run Starling LM 7B for coding?
For coding workloads, Starling LM 7B on Quadro RTX 8000 48GB receives a C grade with 98.0 tok/s and 8K context.
What context window can Starling LM 7B use on Quadro RTX 8000 48GB?
On Quadro RTX 8000 48GB, Starling LM 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
Embed this result▼
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<iframe src="https://willitrunai.com/embed/starling-7b-on-quadro-rtx-8000-48gb" 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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