Will It Run AI

Can DeepSeek R1 Distill 8B run on NVIDIA L4 24GB?

YES — Runs Great

B65Good
Estimated from fit model

DeepSeek R1 Distill 8B needs ~10.4 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~43 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Balanced
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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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 10.4 GB, 43.0 tok/s, Runs well
10.4 GB required24.0 GB available
43% VRAM used

Fit status

Runs well

Decode

43.0 tok/s

TTFT

4507 ms

Safe context

33K

Memory

10.4 GB / 24.0 GB

Memory breakdown

Weights4.9 GB
KV Cache2.0 GB
Runtime1.2 GB
Headroom2.4 GB

See how fast it feels

See how fast it feelsDeepSeek R1 Distill 8B on NVIDIA L4 24GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step. Given information: - Speed = 60 mph (miles per hour) - Time = 2.5 hours Formula: Distance = Speed x Time Calculation: Distance = 60 mph x 2.5 h = 150 miles The train travels 150 miles. Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time. Breakdown: - First half: 1.25 hours at 60 mph = 75 miles - Stop: 20 minutes (no distance) - Second half: 1.25 hours at 60 mph = 75 miles Total distance: 150 miles (same) Total time: 2.5 h + 0.33 h = 2.83 hours Average speed: 150 / 2.83 = 53 mph The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 43.0 tok/s decode · 4.5s TTFT (warm) · 107 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

WorkloadGradeFitDecodeTTFTContext
ChatBRuns well43.0 tok/s2458 ms33K
CodingBRuns well43.0 tok/s4507 ms33K
Agentic CodingBRuns well43.0 tok/s6556 ms33K
ReasoningBRuns well43.0 tok/s5326 ms33K
RAGBRuns well43.0 tok/s8194 ms33K

Quantization options

How DeepSeek R1 Distill 8B (8B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowB61
Q3_K_S
3
3.9 GB
LowB61
NVFP4
4
4.5 GB
MediumB62
Q4_K_M
4
4.9 GB
MediumB62
Q5_K_M
5
5.8 GB
HighB62
Q6_K
6
6.6 GB
HighB63
Q8_0
8
8.6 GB
Very HighB64
F16Best for your GPU
16
16.4 GB
MaximumB66

Get started

Copy-paste commands to run DeepSeek R1 Distill 8B on your machine.

Run

ollama run deepseek-r1:8b

Opciones de mejora

Hardware que ejecuta bien DeepSeek R1 Distill 8B

NVIDIARTX 5090 32GBOpción económica
32 GB VRAM (+8)1792 GB/s (+1492)
B
Sube la velocidad estimada de decodificación alrededor de un 160%.112 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 160%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$1,999 MSRP

NVIDIARTX PRO 4500 Blackwell 32GBMejor relación calidad-precio
32 GB VRAM (+8)896 GB/s (+596)
B
Sube la velocidad estimada de decodificación alrededor de un 160%.112 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 160%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$2,499 MSRP

NVIDIARTX 5000 Ada 32GBMejora NVIDIA
32 GB VRAM (+8)576 GB/s (+276)
B
Sube la velocidad estimada de decodificación alrededor de un 136%.101.5 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 136%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$4,000 MSRP

Frequently asked questions

Can NVIDIA L4 24GB run DeepSeek R1 Distill 8B?

Yes, NVIDIA L4 24GB can run DeepSeek R1 Distill 8B with a B grade (Runs well). Expected decode speed: 43.0 tok/s.

How much VRAM does DeepSeek R1 Distill 8B need?

DeepSeek R1 Distill 8B (8B parameters) requires approximately 10.4 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek R1 Distill 8B?

The recommended quantization for DeepSeek R1 Distill 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek R1 Distill 8B run at on NVIDIA L4 24GB?

On NVIDIA L4 24GB, DeepSeek R1 Distill 8B achieves approximately 43.0 tokens per second decode speed with a time-to-first-token of 4507ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run DeepSeek R1 Distill 8B for coding?

For coding workloads, DeepSeek R1 Distill 8B on NVIDIA L4 24GB receives a B grade with 43.0 tok/s and 33K context.

What context window can DeepSeek R1 Distill 8B use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, DeepSeek R1 Distill 8B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

See all results for NVIDIA L4 24GBSee all hardware for DeepSeek R1 Distill 8B
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