Can the Quadro RTX 6000 run LLM Inference?

Yes. The Quadro RTX 6000 meets the 12 GB VRAM minimum for LLM Inference (it has 24 GB). AIMC fit score: 70/100 (good fit).

How much does it cost to rent the Quadro RTX 6000 for LLM Inference?

The Quadro RTX 6000 rents for $0.170/hr at the cheapest marketplace, with a listing-weighted median of $0.220/hr across 3 authorized partners.

What's the best alternative GPU for LLM Inference?

The top-scoring alternatives for LLM Inference are: A100 PCIe 40GB (fit 100/100), A100 PCIe 80GB (fit 100/100), A100 SXM 40GB (fit 100/100).

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AIMC Fit Analysis · AI

Quadro RTX 6000 for
LLM Inference

Serving large language models for chat, completion, and agentic workloads.

Fit Score

70/100

Good fit

Hourly Rate

$0.22

listing-weighted median

VRAM vs Required

24 / 12 GB

2.0× the minimum

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Is the Quadro RTX 6000 Good for LLM Inference?

Good fit. AIMC's fit score combines VRAM headroom, GPU class match, and FP16 compute against the workload's requirements.

Workstation class is well-suited for LLM Inference
24 GB VRAM is adequate for most llm inference jobs
Compute benchmarks pending — fit is estimated from VRAM and GPU class

What LLM Inference Needs

Background on the workload and its hardware requirements.

LLM inference has very different requirements from training: VRAM still gates which models you can serve, but throughput, latency, and cost-per-token become the dominant economics. Production inference servers like vLLM, TGI (Text Generation Inference), and SGLang use PagedAttention, continuous batching, and speculative decoding to maximize tokens-per-second per dollar.

Quantization is the lever: a Llama 3 70B model in FP16 needs roughly 140 GB, but the same model in INT4 fits in 35 GB and serves at nearly the same quality for most workloads. AWQ, GPTQ, and bitsandbytes are the common quantization toolchains. Smaller models (Llama 3 8B, Mistral 7B, Phi-3) routinely run on a single 24 GB GPU at 50-150 tokens/second.

For consumer inference (single-user, low concurrency), llama.cpp and Ollama run quantized models on hardware as modest as 8 GB. For multi-tenant serving with high concurrency, datacenter GPUs with high memory bandwidth (HBM) dominate the cost-per-token curve.