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GPU × use-case guide · Code assistant

Is the L4 a good GPU for code assistant?

The L4 is a ada NVIDIA GPU with 24 GB GDDR6 (300 GB/s), 121 TFLOPS BF16 / 242 TFLOPS FP8, and a 72 W TDP. Code assistant workloads care most about low time-to-first-token and high single-stream tokens/sec, since developers wait on completions in real time. Here's how the L4 measures up.

VRAM
24 GB
Bandwidth
300 GB/s
BF16
121 TFLOPS
Cheapest
$0.39/hr

What models fit on a single L4?

Weights only, reserving ~25% of the 24 GB for KV cache, activations and fragmentation. ✓ = fits on one card.

ModelBF16FP8INT4
Llama 3.1 8B
Qwen 2.5 14B
Gemma 2 27B
Mixtral 8x7B (MoE)
Llama 3.3 70B
Qwen 2.5 72B
Llama 3.1 405B

Largest single-card fit: Llama 3.1 8B at BF16, Qwen 2.5 14B at FP8, Gemma 2 27B at INT4. Bigger models need tensor-parallel across 8 cards.

L4 for code assistant, specifically

Code assistant is context-heavy, so the KV cache — not the weights — is what fills the 24 GB. On the L4 you'll trade context length against batch size: long prompts mean fewer concurrent requests. Because it's latency-sensitive, run it at low batch sizes on a fast framework (vLLM ≈ 85% MFU) rather than maximising batch. Size it precisely on the calculator.

L4 pricing across providers

ProviderOn-demand $/hrReserved $/hr
tensordock$0.39
vast_ai$0.45
lambda$0.59
runpod$0.69
gcp$0.7$0.49
aws$0.81$0.52

Verdict

At 24 GB, the L4 is a value card best suited to smaller code assistant models (up to Gemma 2 27B with INT4 quantisation). For larger models you'll want more VRAM or multi-GPU.

See full L4specs & pricing, size your model on the calculator, or compare every GPU on the GPU list.