Mac Studio M3 Ultra 96GBOpção econômica
96 GB Unified (+48)
C
Adds memory headroom for longer context windows and future model growth.112 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Can llava llama 3 8b v1 1 run on RTX 6000 Ada 48GB?
YES — Runs Great
C47Usable
Estimated from fit model
llava llama 3 8b v1 1 needs ~11.8 GB VRAM. RTX 6000 Ada 48GB has 48.0 GB. With Q4_K_M quantization, expect ~112 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 11.8 GB, 112.0 tok/s, Runs well
11.8 GB required48.0 GB available
25% VRAM used
Fit status
Runs well
Decode
112.0 tok/s
TTFT
1729 ms
Safe context
634K
Memory
11.8 GB / 48.0 GB
Memory breakdown
Weights4.9 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom4.8 GB
See how fast it feels
See how fast it feelsllava llama 3 8b v1 1 on RTX 6000 Ada 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: 112.0 tok/s decode · 1.7s TTFT (warm) · 280 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 | C | Runs well | 112.0 tok/s | 943 ms | 634K |
| Coding | C | Runs well | 112.0 tok/s | 1729 ms | 634K |
| Agentic Coding | C | Runs well | 112.0 tok/s | 2514 ms | 634K |
| Reasoning | C | Runs well | 112.0 tok/s | 2043 ms | 634K |
| RAG | C | Runs well | 112.0 tok/s | 3143 ms | 634K |
Quantization options
How llava llama 3 8b v1 1 (8B params) fits at each quantization level on RTX 6000 Ada 48GB (48.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C42 |
Q3_K_S | 3 | 3.9 GB | Low | C42 |
NVFP4 | 4 | 4.5 GB | Medium | C42 |
Q4_K_M | 4 | 4.9 GB | Medium | C42 |
Q5_K_M | 5 | 5.8 GB | High | C42 |
Q6_K | 6 | 6.6 GB | High | C42 |
Q8_0 | 8 | 8.6 GB | Very High | C43 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | C45 |
Get started
Copy-paste commands to run llava llama 3 8b v1 1 on your machine.
Run
lms load hf-xtuner--llava-llama-3-8b-v1-1-gguf && lms server startOpções de upgrade
Hardware que roda bem llava llama 3 8b v1 1
Frequently asked questions
Can RTX 6000 Ada 48GB run llava llama 3 8b v1 1?
Yes, RTX 6000 Ada 48GB can run llava llama 3 8b v1 1 with a C grade (Runs well). Expected decode speed: 112.0 tok/s.
How much VRAM does llava llama 3 8b v1 1 need?
llava llama 3 8b v1 1 (8B parameters) requires approximately 11.8 GB of memory with Q4_K_M quantization.
What is the best quantization for llava llama 3 8b v1 1?
The recommended quantization for llava llama 3 8b v1 1 is Q4_K_M, which balances quality and memory efficiency.
What speed will llava llama 3 8b v1 1 run at on RTX 6000 Ada 48GB?
On RTX 6000 Ada 48GB, llava llama 3 8b v1 1 achieves approximately 112.0 tokens per second decode speed with a time-to-first-token of 1729ms using Q4_K_M quantization.
Can RTX 6000 Ada 48GB run llava llama 3 8b v1 1 for coding?
For coding workloads, llava llama 3 8b v1 1 on RTX 6000 Ada 48GB receives a C grade with 112.0 tok/s and 634K context.
What context window can llava llama 3 8b v1 1 use on RTX 6000 Ada 48GB?
On RTX 6000 Ada 48GB, llava llama 3 8b v1 1 can safely use up to 634K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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