Can I Run GLM-5 / GLM-5.1 on NVIDIA GeForce RTX 4090?
Written by Jakub Rusinowski · Last updated September 29, 2026
Yes, but it's tight — GLM-5 32B at Q4_K_M needs 22 GB at 8K context (19.3 GB weights + 2.7 GB KV cache/overhead), leaving ~2 GB of the NVIDIA GeForce RTX 4090's 24 GB, at ~42 tok/s (est.), with room for up to 16K context.
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NVIDIA GeForce RTX 4090 Specs
| VRAM | 24 GB |
| Memory Bandwidth | 1008 GB/s |
GLM-5 32B on the NVIDIA GeForce RTX 4090: VRAM by quantization
| Quant | VRAM needed | Fits 24 GB? | Max context | Whole PC |
|---|---|---|---|---|
| F16 | 66.6 GB | ✗ No | — | — |
| Q8_0 | 36.6 GB | ✗ No | — | — |
| Q6_K | 28.9 GB | ✗ No | — | — |
| Q5_K_M | 25.3 GB | ✗ No | — | — |
| Q4_K_M | 22 GB | ✓ Yes | 16K | — |
| Q3_K_M | 16.3 GB | ✓ Yes | 32K | — |
| Q2_K | 13.2 GB | ✓ Yes | 32K | — |
Assumes an 8K-token context with an f16 KV cache, on an estimated architecture — this model publishes no config we can read. A longer window needs more; a quantized KV cache needs less. “Max context” is the largest window that still fits in 24 GB. Figures are estimates from parameter count, quantization and memory bandwidth — the analyzer lets you tune KV-cache quant and context.
Context costs VRAM too. At Q4_K_M on the NVIDIA GeForce RTX 4090: 8K 22 GB · 32K 27.5 GB ✗ · 125K 49 GB ✗. Past 32K it no longer fits 24 GB — a q8_0 KV cache buys roughly half of that back, which the calculator will price for you.
No compatible GPU? GLM-5 32B on 32 GB of system RAM, CPU only: runs comfortably.
GLM-5 / GLM-5.1 Sizes That Fit the NVIDIA GeForce RTX 4090 (at 8K context)
| GLM-5 32B | Q4_K_M · 22 GB at 8K context · ~42 tok/s (est.) |
| GLM-5 9B | Q4_K_M · 7.4 GB at 8K context · ~115 tok/s (est.) |
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FAQ
Does the NVIDIA GeForce RTX 4090 have enough VRAM for GLM-5 / GLM-5.1?
Yes, but it's tight — GLM-5 32B at Q4_K_M needs 22 GB at 8K context (19.3 GB weights + 2.7 GB KV cache/overhead), leaving ~2 GB of the NVIDIA GeForce RTX 4090's 24 GB, at ~42 tok/s (est.), with room for up to 16K context.
Which quantization of GLM-5 / GLM-5.1 should I use on the NVIDIA GeForce RTX 4090?
GLM-5 32B at Q4_K_M quantization needs 22 GB at 8K context (19.3 GB weights + 2.7 GB KV cache/overhead), estimated ~42 tokens/sec, up to 16K context.
Every GLM-5 / GLM-5.1 size on the NVIDIA GeForce RTX 4090
| Size | VRAM at 8K | Verdict | Speed |
|---|---|---|---|
| GLM-5 744B | 455.2 GB | Does not fit | — |
| GLM-5.1 72B | 46.7 GB | Does not fit | — |
| GLM-5 32B | 22 GB | Runs | ~42 tok/s |
| GLM-5 9B | 7.4 GB | Runs | ~115 tok/s |
GLM-5 / GLM-5.1 on Other GPUs
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VRAM Tier
Troubleshooting
- CUDA out of memory — why it happens and how to fix it
- Which GGUF quant should I download? (Q4 vs Q5 vs Q8)
Buying Guide
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