RTX 5090 32GBコスパ重視32 GB VRAM (+8)1792 GB/s (+1492)
C
Raises estimated decode speed by about 180%.112 tok/sデコード
Raises estimated decode speed by about 180%.
Adds memory headroom for longer context windows and future model growth.
〜$1,999 MSRP
Can dolphin 2.9.4 llama3.1 8b run on NVIDIA L4 24GB?
YES — Runs Great
C47Usable
Estimated from fit model
dolphin 2.9.4 llama3.1 8b needs ~9.4 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~40 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 9.4 GB, 40.0 tok/s, Runs well
9.4 GB required24.0 GB available
39% VRAM used
Fit status
Runs well
Decode
40.0 tok/s
TTFT
4845 ms
Safe context
265K
Memory
9.4 GB / 24.0 GB
Memory breakdown
Weights4.9 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsdolphin 2.9.4 llama3.1 8b on NVIDIA L4 24GB
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: 40.0 tok/s decode · 4.8s TTFT (warm) · 100 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 | 40.0 tok/s | 2643 ms | 265K |
| Coding | C | Runs well | 40.0 tok/s | 4845 ms | 265K |
| Agentic Coding | C | Runs well | 40.0 tok/s | 7047 ms | 265K |
| Reasoning | C | Runs well | 40.0 tok/s | 5726 ms | 265K |
| RAG | C | Runs well | 40.0 tok/s | 8809 ms | 265K |
Inference speed
dolphin 2.9.4 llama3.1 8b inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for dolphin 2.9.4 llama3.1 8b at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~112 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 | 112.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 112.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 112.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 112.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 112.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 112.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 95.1 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 90.2 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 77.5 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 76.8 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 76.8 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 49.2 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 48.7 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 45.1 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 40.7 | Offloads |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 39.6 | Fits |
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 dolphin 2.9.4 llama3.1 8b (8B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C44 |
Q3_K_S | 3 | 3.9 GB | Low | C45 |
NVFP4 | 4 | 4.5 GB | Medium | C45 |
Q4_K_M | 4 | 4.9 GB | Medium | C45 |
Q5_K_M | 5 | 5.8 GB | High | C46 |
Q6_K | 6 | 6.6 GB | High | C46 |
Q8_0 | 8 | 8.6 GB | Very High | C48 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | C50 |
Get started
Copy-paste commands to run dolphin 2.9.4 llama3.1 8b on your machine.
Run
lms load hf-bartowski--dolphin-2-9-4-llama3-1-8b-gguf && lms server startアップグレードオプション
dolphin 2.9.4 llama3.1 8bを快適に動かすハードウェア
RTX 5090 32GBコスパ重視RTX PRO 4500 Blackwell 32GBコスパ最良32 GB VRAM (+8)896 GB/s (+596)
C
Raises estimated decode speed by about 180%.112 tok/sデコード
Raises estimated decode speed by about 180%.
Adds memory headroom for longer context windows and future model growth.
〜$2,499 MSRP
RTX 5000 Ada 32GBNVIDIAアップグレード32 GB VRAM (+8)576 GB/s (+276)
C
Raises estimated decode speed by about 136%.94.4 tok/sデコード
Raises estimated decode speed by about 136%.
Adds memory headroom for longer context windows and future model growth.
〜$4,000 MSRP
Frequently asked questions
Can NVIDIA L4 24GB run dolphin 2.9.4 llama3.1 8b?
Yes, NVIDIA L4 24GB can run dolphin 2.9.4 llama3.1 8b with a C grade (Runs well). Expected decode speed: 40.0 tok/s.
How much VRAM does dolphin 2.9.4 llama3.1 8b need?
dolphin 2.9.4 llama3.1 8b (8B parameters) requires approximately 9.4 GB of memory with Q4_K_M quantization.
What is the best quantization for dolphin 2.9.4 llama3.1 8b?
The recommended quantization for dolphin 2.9.4 llama3.1 8b is Q4_K_M, which balances quality and memory efficiency.
What speed will dolphin 2.9.4 llama3.1 8b run at on NVIDIA L4 24GB?
On NVIDIA L4 24GB, dolphin 2.9.4 llama3.1 8b achieves approximately 40.0 tokens per second decode speed with a time-to-first-token of 4845ms using Q4_K_M quantization.
Can NVIDIA L4 24GB run dolphin 2.9.4 llama3.1 8b for coding?
For coding workloads, dolphin 2.9.4 llama3.1 8b on NVIDIA L4 24GB receives a C grade with 40.0 tok/s and 265K context.
What context window can dolphin 2.9.4 llama3.1 8b use on NVIDIA L4 24GB?
On NVIDIA L4 24GB, dolphin 2.9.4 llama3.1 8b can safely use up to 265K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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