Can I run GLM-5.2 on an RTX 5080 (16GB)?
Computed with the open FitLLM engine — accurate per-layer KV-cache modeling, not a naive estimate. Updated 2026-10-19.
Conditions: Q4_K_M · KV cache F16 · 32K context · Windows + display reserve (2 GB).
Memory breakdown (Q4_K_M, F16 KV, 32K context)
| Model weights | 429 GB |
| KV cache | 2.7 GB |
| Runtime overhead + reserve | 54.9 GB |
| Total used | 487 / 16 GB |
| Short by | 471 GB |
Context at Q4_K_M: does not fit at any context.
Every quantization on the RTX 5080
| Weight quant | Weights | Recommended max ctx | Hard limit | Used @32K |
|---|---|---|---|---|
| Q4_K_M | 429 GB | won't fit | ❌ 487 / 16.0 GB | |
| Q5_K_M | 500 GB | won't fit | ❌ 566 / 16.0 GB | |
| Q6_K | 575 GB | won't fit | ❌ 651 / 16.0 GB | |
| Q8_0 | 745 GB | won't fit | ❌ 841 / 16.0 GB | |
| FP16 | 1403 GB | won't fit | ❌ 1577 / 16.0 GB | |
Recommended max = the longest context that still keeps the 5% headroom · hard limit = the longest that fits at all (KV cache F16).
Lower weight quants free memory at some output-quality cost — Q4 is the common sweet spot; below that quality drops faster.
KV cache is F16 here (llama.cpp default). Drop it to Q8/Q4 (-ctk/-ctv) for more context.
Embed this verdict
Live badge for your README or model card — recomputed by the engine at exactly these conditions, never stale:
[](https://fitllm.run/can-i-run/glm-5-2-on-rtx-5080)
Or from your terminal (exit 0/1 — works as a pre-download guard):
npx fitllm "GLM-5.2" --gpu "RTX 5080" --quant Q4_K_M --ctx 32768 --kv 16Why most VRAM calculators get this wrong
GLM-5.2 uses MLA (Multi-head Latent Attention): K/V are compressed into a single low-rank latent (512 + 64 RoPE dims) shared across all heads — cached once, not per-head K and V. Naive "2 × heads × head_dim × layers" formulas over-count its KV cache by an order of magnitude.
What fits on the RTX 5080 instead
same GPU Models that fit on the RTX 5080: Gemma 4 e2b, Gemma 4 e4b, Gemma 4 12b, Llama-3.2-3B-Instruct, Llama-3.1-8B-Instruct, MiniCPM5-1B, Qwen3-0.6B, Qwen3-1.7B, Llama-3.2-1B-Instruct, Gemma-3-1B-it, Spark-X2.5-4B, MiniCPM5-2B.
same model GPUs that run GLM-5.2: —.
Reproduce it
GLM-5.2 = 753B (40B active, MoE), 78 layers. The RTX 5080 has 16GB / 960GB/s. Same math, open source: fitllm-engine. GGUF bpw from llama.cpp.
All numbers are computed by the open-source fitllm-engine (MIT) from official model config.json values — reproduce or audit them yourself. Estimates; real usage varies with runtime (llama.cpp / MLX / Ollama), driver and display. Found a mismatch? Report it. · FitLLM home