Interactive Utility
GLM-5.3 Flash (321B) VRAM Calculator
Estimate the memory needed to run GLM-5.3 Flash (321B) locally. Compare quantization levels and context lengths, then check the assumptions before choosing hardware.
How much VRAM does GLM-5.3 Flash (321B) need? GLM-5.3 Flash (321B) (321.3B parameters) needs about 204.3 GB of VRAM at Q4_K_M and about 652.1 GB at FP16, with an 8k-token context. Estimate from parameter count, bits per weight, KV cache and 1.5 GB of runtime overhead — not a benchmark.
Model
Custom: billion
Smaller modeled weight footprint; check model-specific quality.
Estimated memory footprint
- Weights (Q4_K_M)
- 194.8 GB
- KV cache (8k)
- 8.0 GB
- Runtime overhead
- 1.5 GB
- Estimated system RAM to load
- 197 GB
- Estimated model file size
- 204.5 GB
Compare with your available memory
Weights exceed available memory
The modeled weights alone exceed your usable memory. Try a smaller model, stronger quantization, or more memory.
GLM-5.3 Flash (321B) memory requirements by quantisation
Estimated at an 8k-token context. GLM-5.3 Flash (321B) is 321.3B parameters; the arithmetic and its assumptions are set out below.
| Quantisation | Weights | Total VRAM |
|---|---|---|
| FP16 (unquantised) | 642.6 GB | 652.1 GB |
| Q8_0 (8-bit) | 341.4 GB | 350.9 GB |
| Q6_K (6-bit) | 265.1 GB | 274.6 GB |
| Q5_K_M (5-bit) | 228.9 GB | 238.4 GB |
| Q4_K_M (4-bit) | 194.8 GB | 204.3 GB |
| Q3_K_M (3-bit) | 156.6 GB | 166.1 GB |
Arithmetic over stated assumptions, not a benchmark. Weights are parameter count times bits-per-weight; the KV cache term assumes grouped-query attention rather than this model's verified KV-head count; 1.5GB is reserved for runtime overhead. Confirm the model configuration and usable device memory before purchasing hardware.
Size a related model
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How this math works
Local inference depends on several constraints. Memory capacity affects whether a model loads; memory bandwidth, compute and runtime settings also affect generation speed.
- Model Weights: At FP16 (unquantized), every 1 Billion parameters requires ~2GB of VRAM. At Q4_K_M (4-bit quantization), that drops to about 0.6GB per 1B parameters.
- KV Cache (Context Window): As you feed text into the model, it stores attention states in memory. A 32k context window on a 70B model requires several extra Gigabytes of RAM independent of the model weights.
- Overhead: This estimate reserves 1.5 GB for the runtime. Actual allocation depends on the device, framework and workload, so compare against usable rather than advertised memory.