Model × hardware check
Can GLM-4.7-Flash run on NVIDIA GeForce RTX 5090 · 32 GB?
Yes, this is a comfortable estimate at 8,192 tokens. a 30B-total, 3B-active MoE built for lightweight local reasoning and coding The RTX 5090's 32 GB frame buffer is the largest consumer tier in this catalog, with 1792 GB/s of memory bandwidth.
Short answer
Comfortable fit
At least 20% memory headroom remains after weights, KV cache, and runtime overhead.
- Available VRAM
- 32 GB
- Estimated requirement
- 23.4 GB
- Headroom
- +8.6 GB
- Fit ratio
- 137%
- Recommended quantization
- Q5_K_M
- Context ceiling
- 167,922 tokens
- Speed estimate
- Estimate · 460.4–627.8 tok/s
Estimate · decode · llama.cpp/MLX · 8,192-token context This is a bandwidth model, not a measured benchmark.
What the numbers say
The selected GLM-4.7-Flash package uses 21.4 GB of weight files. The 8,192 tokens KV cache estimate is 0.4 GB, and the formula adds 1.5 GB of runtime overhead. That produces a 23.4 GB requirement against 32 GB of VRAM.
The first 32 GB consumer-class target in this catalog. The extra capacity is most useful for larger quantized models or longer contexts.
The context ceiling shown above is the point where the same formula reaches the machine's capacity. It is not a promise that the backend can allocate that full context after the operating system and other applications take their share.
Quantization trade-offs at 8,192 tokens
| Quantization | Weight files | 8,192 tokens requirement | Fit on this machine | Context ceiling | Speed estimate | Ollama |
|---|---|---|---|---|---|---|
| Q3_K_M | 14.62 GB | 16.6 GB | Comfortable fit · 193% | 202,752 tokens | Estimate · 674.4–919.6 tok/s | No verified tag |
| MXFP4 | 16.97 GB | 18.9 GB | Comfortable fit · 169% | 202,752 tokens | Estimate · 580.9–792.1 tok/s | No verified tag |
| Q4_K_M | 18.31 GB | 20.3 GB | Comfortable fit · 158% | 202,752 tokens | Estimate · 538.2–733.9 tok/s | ollama run glm-4.7-flash:q4_K_M |
| Q5_K_M | 21.41 GB | 23.4 GB | Comfortable fit · 137% | 167,922 tokens | Estimate · 460.4–627.8 tok/s | No verified tag |
| Q8_0 | 31.84 GB | 33.8 GB | Won't fit · 95% | Does not fit | Not applicable — does not fit | ollama run glm-4.7-flash:q8_0 |
For MoE models, a tight CPU-offload result means the weights fit in system memory even when they do not fit in accelerator memory. That does not increase the accelerator context ceiling.
Run commands
lms get "GLM-4.7-Flash"@q5_k_m
lms load <model-key-from-lms-ls> --gpu max --context-length 8192llama-cli -hf unsloth/GLM-4.7-Flash-GGUF:Q5_K_M -c 8192 -ngl 99 -p "Your prompt"mlx_lm.generate --model mlx-community/GLM-4.7-Flash-4bit --prompt "Your prompt" --max-tokens 8192Commands are examples, not a guarantee that the selected quantization tag is available in every runtime. Confirm the package name and license on the linked model page.
Public speed checks
No verified measurement yet. The estimate above is deliberately labeled as an estimate rather than a benchmark.
If it does not fit
This pairing already clears the comfort threshold
The current estimate leaves +8.6 GB. A longer context or another GPU application can reduce that margin, so verify free memory before relying on it.
Low-cost setup upgrades
Useful upgrades around NVIDIA GeForce RTX 5090 · 32 GB
These are optional setup improvements, not a claim that an accessory can increase GPU or unified-memory capacity.
External NVMe SSD for model storage
Keeps large GGUF and MLX files off the boot drive. Fast storage reduces load time, but it does not increase the memory available for inference.
Search current listings on AmazonDesktop RAM upgrade kit
Useful for MoE CPU offload and keeping the operating system out of VRAM. Match the motherboard's memory type, slot layout, capacity limit, and validated part numbers.
Search current listings on AmazonGPU support bracket
A lower-cost mechanical upgrade for heavy three-slot cards. Check clearance, mounting height, and whether the bracket blocks fans or case panels.
Search current listings on AmazonAs an Amazon Associate I earn from qualifying purchases. Links are sponsored search links and do not change the price you pay. Affiliate disclosure.
Where this estimate can break
- The 30B stored experts determine memory, not the 3B active path. At 8K, Q4_K_M plus KV cache and overhead is about 20.3 GB, which is tight on a 24 GB GPU and does not fit a 16 GB device.
- Use Q3_K_M when 24 GB is a hard ceiling. The lower-bit file trades quality for enough headroom to avoid memory pressure.
- Official runtime documentation covers vLLM, SGLang, and Transformers. Ollama, llama.cpp, and community MLX builds are useful local paths but are not the only upstream-supported runtimes.
- Other GPU applications, display buffers, and the operating system reduce usable memory.
- Longer prompts, concurrent requests, and a different backend can raise peak memory.
Compare this pairing
FAQ
Questions about this pairing.
How much memory does GLM-4.7-Flash need on NVIDIA GeForce RTX 5090 · 32 GB?
At 8,192 tokens with Q5_K_M, the estimate is 23.4 GB: 21.4 GB of weights, 0.4 GB of KV cache, and 1.5 GB of runtime overhead. The available VRAM is 32 GB.
What context length can NVIDIA GeForce RTX 5090 · 32 GB handle with this model?
The calculated ceiling is 167,922 tokens for this quantization, capped by the model's native 202,752-token context. Actual usable context can be lower after the OS, other applications, and backend workspace.
How does quantization change the memory requirement on NVIDIA GeForce RTX 5090 · 32 GB?
At 8,192 tokens, the published variants calculate as follows: Q3_K_M: 16.6 GB, comfortable fit; MXFP4: 18.9 GB, comfortable fit; Q4_K_M: 20.3 GB, comfortable fit; Q5_K_M: 23.4 GB, comfortable fit; Q8_0: 33.8 GB, won't fit. A lower-bit file can move a result from too large to tight, but quality and kernel support may differ.
What can I run if GLM-4.7-Flash does not fit on NVIDIA GeForce RTX 5090 · 32 GB?
This selection already clears the comfort threshold. If you later increase the context, monitor the ceiling of 167,922 tokens; smaller alternatives include Flint 40B, NVIDIA Nemotron 3.5 Bolt E4B, LFM3-1.7B.
Is the speed estimate for GLM-4.7-Flash on NVIDIA GeForce RTX 5090 · 32 GB measured?
The site's Estimate · 460.4–627.8 tok/s range is an estimate from 3B active parameters, 1792 GB/s accelerator memory bandwidth, and the selected quant at 8,192 tokens. It is not a benchmark. No public measurement is attached to this exact runtime and context.