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Can NVIDIA GeForce RTX 5090 run Qwen3.8-27B?

This page was written and translated automatically by AI and checked by software against its sources, with no human editing. Specifications come from the sources listed; memory requirements and speeds are estimates calculated with published formulas. How we use AI

Fits By llama.cpp’s default load check, Qwen3.8-27B fits on NVIDIA GeForce RTX 5090 at Q6_K with a context of 8,192 tokens. One card needs 24.8 GiB available for “Fits”.

At Q6_K, the longest context that fits is 75,520 tokens.

“Fits” means llama.cpp's default --fit load check would place all layers on the GPU: our estimate of llama.cpp's buffers, plus its error margin, plus llama.cpp's 1 GiB per-card reserve, is within the memory counted as available. It does not promise that peak memory while generating fits. How results are decided

Values updated Oct 19, 2026 · Calculator

Run profile: llama.cpp, CUDA back end (NVIDIA discrete GPU), all layers on the GPU, Flash Attention on, micro-batch (-ub) 512, F16 KV cache, context (-c) as shown; “Fits” is judged with llama-server’s default 4 parallel sequences (-np auto, unified KV cache) and “Doesn’t fit” with 1 sequence; idle GPU with no other programs using its memory.

Memory counted as available for “Fits” (C_low): 29.25 GiB per card = 32 GiB nominal − 659 MiB (nominal minus the total that llama.cpp reported for this GPU in public logs) − 2,112 MiB (the largest start-up and runtime use seen in this profile’s public logs), rounded down to a multiple of 64 MiB.

These results apply to the configuration seen in public logs, where llama.cpp reported 32,109 MiB of total memory for this GPU. Settings that reduce usable memory (for example ECC, where the card supports it) are not covered.

Memory used for “Doesn’t fit” (C_high): the full 32 GiB per card. Run profiles and their measurements

Memory needed by quantization and context

Available memory needed for “Fits” (X, GiB per card) and the result on NVIDIA GeForce RTX 5090.
Quantization4,0968,19232,768131,072
BF16 52.6 GiB Doesn't fit52.9 GiB Doesn't fit54.5 GiB Doesn't fit61.0 GiB Doesn't fit
Q8_0 29.2 GiB Fits29.4 GiB Tight31.1 GiB Tight37.6 GiB Tight
Q6_K 24.5 GiB Fits24.8 GiB Fits26.4 GiB Fits32.9 GiB Tight
Q5_K_M 21.8 GiB Fits22.1 GiB Fits23.7 GiB Fits30.2 GiB Tight
Q4_K_M 18.5 GiB Fits18.8 GiB Fits20.4 GiB Fits27.0 GiB Fits
Q4_0 17.5 GiB Fits17.7 GiB Fits19.4 GiB Fits25.9 GiB Fits
IQ4_XS 16.6 GiB Fits16.9 GiB Fits18.5 GiB Fits25.1 GiB Fits
Q3_K_M 14.6 GiB Fits14.9 GiB Fits16.5 GiB Fits23.0 GiB Fits
IQ3_XXS 13.5 GiB Fits13.8 GiB Fits15.4 GiB Fits21.9 GiB Fits
Q2_K 12.2 GiB Fits12.5 GiB Fits14.1 GiB Fits20.6 GiB Fits
IQ2_M 11.9 GiB Fits12.2 GiB Fits13.8 GiB Fits20.3 GiB Fits
IQ2_XXS 10.3 GiB Fits10.6 GiB Fits12.2 GiB Fits18.7 GiB Fits

How fast can it generate?

At Q6_K, the theoretical ceiling is about 77.6 tokens per second (1,792 GB/s ÷ 23.10 GB read per token).

The speed ceiling is memory bandwidth divided by the bytes read per generated token. It is a theoretical upper bound; public llama.cpp benchmarks typically reach about half to four-fifths of it, and small models and mixture-of-experts models often less. Public benchmarks

Alternatives

Other 32 GB GPUs where Qwen3.8-27B fits

No other GPU with the same memory size fits this model.

Sources