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LLM Inference Latency vs VRAM Memory Consumption Matrix

Name: LLM Inference Latency vs VRAM Memory Consumption Matrix
Brand: Sella
SKU: 6a68dfd44752935fde8b4427
Availability: InStock

Empirical benchmark matrix mapping tokens/sec throughput across Llama 3, Qwen 2.5, DeepSeek, and Mistral across TensorRT-LLM, vLLM, and Ollama backends.

#benchmarks#gpu#vllm#ollama#throughput

Preview · detected sample rows

csv

run_id	model_name	inference_engine	gpu_hardware	quantization_format	vram_peak_gb	ttft_ms	throughput_tokens_per_sec	concurrency_clients	gpu_utilization_pct
RUN_1000	Llama-3.1-8B	vLLM	NVIDIA_H100_SXM	FP16	14.2	40	115.5	4	98.5
RUN_1001	Llama-3.1-70B	TensorRT-LLM	NVIDIA_RTX_4090	Q4_K_M	22.7	49	147.8	8	98.2
RUN_1002	Qwen-2.5-32B	Ollama	NVIDIA_A100_80GB	FP16	31.2	57	160.5	12	97.5
RUN_1003	Mistral-Nemo-12B	TGI	NVIDIA_H100_SXM	Q4_K_M	39.7	63	145.9	16	96.2
RUN_1004	DeepSeek-V2.5	LMDeploy	NVIDIA_RTX_4090	FP16	48.2	64	112.9	20	94.7
RUN_1005	Llama-3.1-8B	vLLM	NVIDIA_A100_80GB	Q4_K_M	14.2	62	81.4	24	92.9
RUN_1006	Llama-3.1-70B	TensorRT-LLM	NVIDIA_H100_SXM	FP16	22.7	56	70.7	4	91.1
RUN_1007	Qwen-2.5-32B	Ollama	NVIDIA_RTX_4090	Q4_K_M	31.2	48	87.1	8	89.5
RUN_1008	Mistral-Nemo-12B	TGI	NVIDIA_A100_80GB	FP16	39.7	38	120.7	12	88.1
RUN_1009	DeepSeek-V2.5	LMDeploy	NVIDIA_H100_SXM	Q4_K_M	48.2	28	151.2	16	87.1
RUN_1010	Llama-3.1-8B	vLLM	NVIDIA_RTX_4090	FP16	14.2	21	160.0	20	86.6
RUN_1011	Llama-3.1-70B	TensorRT-LLM	NVIDIA_A100_80GB	Q4_K_M	22.7	16	141.8	24	86.6
RUN_1012	Qwen-2.5-32B	Ollama	NVIDIA_H100_SXM	FP16	31.2	15	107.7	4	87.1
RUN_1013	Mistral-Nemo-12B	TGI	NVIDIA_RTX_4090	Q4_K_M	39.7	17	78.2	8	88.1

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LLM Inference Latency vs VRAM Memory Consumption Matrix

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