RTX 5090 32GBコスパ重視32 GB VRAM (+8)1792 GB/s (+1446)
C
Raises estimated decode speed by about 155%.109.2 tok/sデコード
Raises estimated decode speed by about 155%.
Adds memory headroom for longer context windows and future model growth.
〜$1,999 MSRP
Can EXAONE 3.5 7.8B Instruct i1 run on Tesla P40 24GB?
YES — Runs Great
C47Usable
Estimated from fit model
EXAONE 3.5 7.8B Instruct i1 needs ~9.3 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~43 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) — 9.3 GB, 42.9 tok/s, Runs well
9.3 GB required24.0 GB available
39% VRAM used
Fit status
Runs well
Decode
42.9 tok/s
TTFT
4512 ms
Safe context
274K
Memory
9.3 GB / 24.0 GB
Memory breakdown
Weights4.8 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsEXAONE 3.5 7.8B Instruct i1 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: 42.9 tok/s decode · 4.5s TTFT (warm) · 107 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 | 42.9 tok/s | 2461 ms | 274K |
| Coding | C | Runs well | 42.9 tok/s | 4512 ms | 274K |
| Agentic Coding | C | Runs well | 42.9 tok/s | 6563 ms | 274K |
| Reasoning | C | Runs well | 42.9 tok/s | 5333 ms | 274K |
| RAG | C | Runs well | 42.9 tok/s | 8204 ms | 274K |
Quantization options
How EXAONE 3.5 7.8B Instruct i1 (7.800000190734863B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.0 GB | Low | C44 |
Q3_K_S | 3 | 3.8 GB | Low | C45 |
NVFP4 | 4 | 4.4 GB | Medium | C45 |
Q4_K_M | 4 | 4.8 GB | Medium | C45 |
Q5_K_M | 5 | 5.6 GB | High | C46 |
Q6_K | 6 | 6.4 GB | High | C46 |
Q8_0 | 8 | 8.3 GB | Very High | C47 |
F16Best for your GPU | 16 | 16.0 GB | Maximum | C50 |
Get started
Copy-paste commands to run EXAONE 3.5 7.8B Instruct i1 on your machine.
Run
lms load hf-mradermacher--exaone-3-5-7-8b-instruct-i1-gguf && lms server startアップグレードオプション
EXAONE 3.5 7.8B Instruct i1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 155%.109.2 tok/sデコード
Raises estimated decode speed by about 155%.
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%.96.8 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 EXAONE 3.5 7.8B Instruct i1?
Yes, Tesla P40 24GB can run EXAONE 3.5 7.8B Instruct i1 with a C grade (Runs well). Expected decode speed: 42.9 tok/s.
How much VRAM does EXAONE 3.5 7.8B Instruct i1 need?
EXAONE 3.5 7.8B Instruct i1 (7.800000190734863B parameters) requires approximately 9.3 GB of memory with Q4_K_M quantization.
What is the best quantization for EXAONE 3.5 7.8B Instruct i1?
The recommended quantization for EXAONE 3.5 7.8B Instruct i1 is Q4_K_M, which balances quality and memory efficiency.
What speed will EXAONE 3.5 7.8B Instruct i1 run at on Tesla P40 24GB?
On Tesla P40 24GB, EXAONE 3.5 7.8B Instruct i1 achieves approximately 42.9 tokens per second decode speed with a time-to-first-token of 4512ms using Q4_K_M quantization.
Can Tesla P40 24GB run EXAONE 3.5 7.8B Instruct i1 for coding?
For coding workloads, EXAONE 3.5 7.8B Instruct i1 on Tesla P40 24GB receives a C grade with 42.9 tok/s and 274K context.
What context window can EXAONE 3.5 7.8B Instruct i1 use on Tesla P40 24GB?
On Tesla P40 24GB, EXAONE 3.5 7.8B Instruct i1 can safely use up to 274K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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