RTX 5090 for Local LLMs: 32GB VRAM, What Fits

Overview

The RTX 5090 for local LLMs is a 32 GB GDDR7 card that holds a 32B-class model at Q4_K_M to Q6_K with room for context, and does not hold a 70B-class model at Q4_K_M on one card. NVIDIA’s RTX 5090 page lists 32 GB GDDR7 on a 512-bit interface, 21760 CUDA cores and 575 W total graphics power. Speed in tokens per second is unknown on this page: NVIDIA publishes no LLM figure for the card, and none is borrowed from a blog. The fit math follows methodology and llm-quantization-explained; the 1 to 2 GB runtime overhead is an assumption, not a measurement.

How much VRAM does the RTX 5090 have?

32 GB of GDDR7, per NVIDIA’s RTX 5090 page read on 2026-10-13. The same page lists a 512-bit memory interface, 21760 CUDA cores, 2.41 GHz boost clock, PCI Express Gen 5, 575 W total graphics power and 1000 W required system power (a footnote on the page ties 1000 W to a Ryzen 9 9950X system). Memory bandwidth is not listed on the page, so it is unknown here. For the 24 GB cards it is usually compared with, see rtx-3090-vs-4090-vs-5090-llm.

What LLM can I run on an RTX 5090?

Formulas: weights_GB = params x bits / 8 / 1e9; KV bytes = 2 x layers x kv_heads x head_dim x context x bytes_per_value; plus a labelled 1 to 2 GB overhead assumption. Bits per weight are from the llama.cpp quantize README: Q4_K_M 4.8944, Q5_K_M 5.7036, Q6_K 6.5633, Q8_0 8.5008. Layers and KV heads come from each Qwen2.5 config.json (head dimension 128, bfloat16, so 2 bytes per value); parameter counts come from the model cards (7B, 14B, 32B, 72B). Totals are weights + KV + 1 to 2 GB, in decimal GB.

Worked example, 32B Q6_K at 8,192 context: 26.6634 + 2.1475 + 1 to 2 = 29.81 to 30.81 GB, which fits; at 32,768 context it does not. Llama 3.1 8B Q4_K_M is a 4.9 GB file per the README, so it fits with plenty to spare; its KV term is unknown here because its config.json was not read. More on this size class: 32gb-vram-llm.

Can an RTX 5090 run a 70B model?

Not at Q4_K_M on one card. The README lists Llama 3.1 70B Q4_K_M at 43.1 GB, and Qwen2.5-72B Q4_K_M weights are 72.7e9 x 4.8944 / 8 / 1e9 = 44.4779 GB. Against 32 GB that is a shortfall of at least 43.1 - 32 = 11.1 GB before any KV cache. A lower quant than Q4_K_M is not sized on this page; its size is unknown here. Options are partial CPU offload, two cards, or a larger card: see vram-for-70b-model, rtx-5090-vs-rtx-pro-6000 and rtx-5090-vs-dual-rtx-5060-ti.

Is the RTX 5090 worth it for local LLMs?

It is worth it if your target model needs more than 24 GB and no more than 32 GB. Its measurable edge over a 24 GB card is capacity: 32B Q4_K_M at 32,768 context needs 29.47 to 30.47 GB, which a 24 GB card cannot hold. If your models already fit in 24 GB, the extra 8 GB buys nothing for fit. Price is not stated here: check current retailer price. Speed is unknown, so do not buy it on a claimed tokens-per-second number from this page.

RTX 5090 vs RTX 5080 for LLMs

NVIDIA’s 50-series compare page lists the RTX 5080 at 16 GB GDDR7, 256-bit, 10752 CUDA cores, 360 W and 850 W required system power, against the 5090’s 32 GB, 512-bit, 21760 cores, 575 W and 1000 W. For LLMs the 16 GB figure matters most: Qwen2.5-14B Q8_0 weights alone are 15.6202 GB, so with the assumed 1 to 2 GB overhead it does not fit a 16 GB card. See rtx-3090-vs-4090-vs-5090-llm for the 24 GB comparison.

Why does vLLM say no kernel image is available on an RTX 5090?

The exact “no kernel image” error text was not found in the sources read, so its cause is unknown here. What the sources do say: the 5090 is a Blackwell consumer card, and the vLLM documentation issue #14452, “Steps to run vLLM on your RTX5080 or 5090!”, builds in an NGC container (nvcr.io/nvidia/pytorch:25.02-py3) that provides CUDA 12.8 and PyTorch 2.6 so nvcc can compile for Blackwell. The post says the Flash Attention 3 backend does not work with Blackwell yet and to set VLLM_FLASH_ATTN_VERSION=2 if you hit problems. A log in the thread shows “Device capability 12.0 not supported”, but that line is a non-fatal SymmMemCommunicator warning, not the kernel-image error. The issue was closed as not planned, so it is not a current install guide; check the vLLM docs for your version. Commenters also report multi-GPU 5090 problems with NCCL and no confirmed fix in the part of the thread read.

What power does an RTX 5090 need under LLM load?

NVIDIA’s page lists 575 W total graphics power, “850W Minimum” and 1000 W required system power, and power inputs of “4x PCIe 8-pin cables (adapter in box) OR 1x 600 W PCIe Gen 5 cable”. The page also says the card supports 3x or 4x PCIe 8-pin cables via the included adapter, or a single PCIe Gen 5 cable rated 450 W or greater. Whether the connector is a risk under sustained LLM load is unknown here: no primary source on that was read. Undervolting results are also unknown here. For PSU planning see multi-gpu-pcie-and-psu.

Questions

What LLM can I run on an RTX 5090?

Models up to about 32B at Q4_K_M to Q6_K. By the formula above, Qwen2.5-32B Q4_K_M at 32,768 context is 29.47 to 30.47 GB, and Q6_K fits at 8,192 context (29.81 to 30.81 GB). Q8_0 of a 32B (34.5345 GB of weights) does not fit.

How much VRAM does the RTX 5090 have?

32 GB GDDR7 on a 512-bit interface, per NVIDIA’s RTX 5090 page. Memory bandwidth is not listed there, so it is unknown here.

Can an RTX 5090 run a 70B model?

Not on one card at Q4_K_M. Llama 3.1 70B Q4_K_M is a 43.1 GB file per the llama.cpp README, over the card’s 32 GB before KV cache.

Is it worth getting a 5090 for my needs?

Yes if your model needs 24 to 32 GB, such as a 32B at Q4_K_M with long context. No if your models fit in 24 GB. Check current retailer price; speed is unknown here.

How fast is the RTX 5090 for LLMs?

Unknown. NVIDIA’s RTX 5090 page publishes no LLM tokens-per-second figure, and this page does not quote one without a linked source that names the machine and model.

Why does vLLM say no kernel image is available on an RTX 5090?

The cause is not confirmed in the sources read. Issue #14452 builds vLLM against CUDA 12.8 and PyTorch 2.6 so it compiles for Blackwell, and says Flash Attention 3 does not work on Blackwell yet (set VLLM_FLASH_ATTN_VERSION=2 if you hit problems). The thread’s “Device capability 12.0 not supported” line is a non-fatal warning. Check the vLLM docs for your version.

Is the 16-pin cable a risk under LLM load?

Unknown here. NVIDIA’s page lists the 575 W card power and either four 8-pin cables via the included adapter or a 600 W PCIe Gen 5 cable, and says nothing on load risk.

Sources read on 2026-10-13

• NVIDIA RTX 5090: 32 GB GDDR7, 512-bit, 21760 cores, 2.41 GHz boost, 575 W, 1000 W and 850W Minimum, power cables; no tokens per second, no bandwidth.
• NVIDIA 50-series compare: RTX 5080 16 GB GDDR7, 256-bit, 10752 cores, 360 W, 850 W. The 50-series overview has no spec table.
• vLLM issue 14452: NGC container with CUDA 12.8 / PyTorch 2.6, “Flash Attention 3 backend doesn’t work with Blackwell yet”, VLLM_FLASH_ATTN_VERSION=2, NCCL multi-GPU comments, “Device capability 12.0 not supported” as a non-fatal warning, closed as not planned (first 100,000 of 126,821 characters read).
• llama.cpp quantize README: bits per weight, Llama 3.1 8B 4.9 GB and 70B 43.1 GB at Q4_K_M.
• Qwen2.5 7B, 14B, 32B and 72B model cards (7.61B, 14.7B, 32.5B, 72.7B parameters) and config.json files (layers, KV heads, hidden size, attention heads, bfloat16) re-read on 2026-10-13.
• The r/LocalLLM 16-pin thread could not be fetched; nothing on this page is taken from it.

Related

• 32gb-vram-llm covers every 32 GB card.
• vram-for-70b-model is the 70B walk-through.
• rtx-3090-vs-4090-vs-5090-llm compares three NVIDIA cards.
• rtx-5090-vs-rtx-pro-6000 and rtx-5090-vs-dual-rtx-5060-ti compare alternatives.
• multi-gpu-pcie-and-psu covers power and PCIe.
• vram-requirements, llm-quantization-explained and methodology are the formulas and rules.