RTX 5090 32GBコスパ重視32 GB VRAM (+8)1792 GB/s (+1446)
C
Raises estimated decode speed by about 168%.112 tok/sデコード
Raises estimated decode speed by about 168%.
Adds memory headroom for longer context windows and future model growth.
〜$1,999 MSRP
Can Meta Llama 3.1 8B Instruct run on Tesla P40 24GB?
YES — Runs Great
C48Usable
Estimated from fit model
Meta Llama 3.1 8B Instruct needs ~9.4 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~42 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, 41.8 tok/s, Runs well
9.4 GB required24.0 GB available
39% VRAM used
Fit status
Runs well
Decode
41.8 tok/s
TTFT
4628 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 feelsMeta Llama 3.1 8B Instruct on Tesla P40 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: 41.8 tok/s decode · 4.6s TTFT (warm) · 105 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 | 41.8 tok/s | 2524 ms | 265K |
| Coding | C | Runs well | 41.8 tok/s | 4628 ms | 265K |
| Agentic Coding | C | Runs well | 41.8 tok/s | 6732 ms | 265K |
| Reasoning | C | Runs well | 41.8 tok/s | 5470 ms | 265K |
| RAG | C | Runs well | 41.8 tok/s | 8415 ms | 265K |
Quantization options
How Meta Llama 3.1 8B Instruct (8B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C45 |
Q3_K_S | 3 | 3.9 GB | Low | C45 |
NVFP4 | 4 | 4.5 GB | Medium | C45 |
Q4_K_M | 4 | 4.9 GB | Medium | C46 |
Q5_K_M | 5 | 5.8 GB | High | C46 |
Q6_K | 6 | 6.6 GB | High | C47 |
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 Meta Llama 3.1 8B Instruct on your machine.
Run
lms load hf-maziyarpanahi--meta-llama-3-1-8b-instruct-gguf && lms server startアップグレードオプション
Meta Llama 3.1 8B Instruct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 (+550)
C
Raises estimated decode speed by about 168%.112 tok/sデコード
Raises estimated decode speed by about 168%.
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 (+230)
C
Raises estimated decode speed by about 126%.94.4 tok/sデコード
Raises estimated decode speed by about 126%.
Adds memory headroom for longer context windows and future model growth.
〜$4,000 MSRP
Frequently asked questions
Can Tesla P40 24GB run Meta Llama 3.1 8B Instruct?
Yes, Tesla P40 24GB can run Meta Llama 3.1 8B Instruct with a C grade (Runs well). Expected decode speed: 41.8 tok/s.
How much VRAM does Meta Llama 3.1 8B Instruct need?
Meta Llama 3.1 8B Instruct (8B parameters) requires approximately 9.4 GB of memory with Q4_K_M quantization.
What is the best quantization for Meta Llama 3.1 8B Instruct?
The recommended quantization for Meta Llama 3.1 8B Instruct is Q4_K_M, which balances quality and memory efficiency.
What speed will Meta Llama 3.1 8B Instruct run at on Tesla P40 24GB?
On Tesla P40 24GB, Meta Llama 3.1 8B Instruct achieves approximately 41.8 tokens per second decode speed with a time-to-first-token of 4628ms using Q4_K_M quantization.
Can Tesla P40 24GB run Meta Llama 3.1 8B Instruct for coding?
For coding workloads, Meta Llama 3.1 8B Instruct on Tesla P40 24GB receives a C grade with 41.8 tok/s and 265K context.
What context window can Meta Llama 3.1 8B Instruct use on Tesla P40 24GB?
On Tesla P40 24GB, Meta Llama 3.1 8B Instruct 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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