Can I run GLM-5.3-Flash on an RTX 3060 12GB?
Key facts
No: it needs about 201 GB and this card has 12 GB of graphics memory.
It needs about 200.7 GB at standard quality (estimate). Checked . Unsloth UD-Q4_K_XL GGUF.
Updated · GLM-5.3-Flash on every card
How much memory it needs
That is about 17.6 times what this card has free.
What runs GLM-5.3-Flash instead
Nothing among the 43 graphics cards, PCs and Macs on our list runs it fully.
See our picks for GLM-5.3-Flash in Find hardware. Add your budget to see cheaper machines.
For comparison, an RTX 3060 12GB costs $292€261, used (median of sold listings). Card details
GLM-5.3-Flash on other graphics cards
Doesn't fit10
Not enough graphics memory: it needs about 200.7 GB.
- RTX 3060 12GBhas 12 GB
- RTX 4060 Ti 16GBhas 16 GB
- RTX 3090has 24 GB
- RTX 4090has 24 GB
- RTX 5080has 16 GB
- RX 9060 XT 16GBhas 16 GB
- RTX 5090has 32 GB
- RX 7900 XTXhas 24 GB
- Dual RTX 3090has 48 GB
- Dual RTX 4090has 48 GB
More about the model itself, what it is good at and its licence: GLM-5.3-Flash.
THE FIT LIST · FREE
Will the next model beat GLM-5.3-Flash on your 128 GB? We'll tell you when it lands.
GLM-5.3-Flash needs 200.7 GB at Q4. When a new open model would fit 128 GB, or the cards and Macs that run it change price, The Fit List tells you, with the memory it needs and dated EU/US prices. Plus a free GPU buying cheat sheet (PDF) now.
The numbers behind this answer
A model comes in several sizes. Q4 is the standard one most people use; the bigger ones answer a little better but need more memory. Each row adds up the model file, the memory a conversation takes, and some working space, and compares the total with what is free on this card.
Swipe the table sideways to see every column.
| Quality | Conversation | Result | Model file | Conversation memory | Working space | Total needed | Free memory |
|---|---|---|---|---|---|---|---|
| Q4 · standard (smallest that keeps quality) | ~12 pages8,192 tokens | Doesn't fit | 199.7 GBmeasured file | 0.2 GB | 0.8 GB | 200.7 GB | 11.4 GB |
| Q5 · a bit sharper, a bit bigger | ~12 pages8,192 tokens | Doesn't fit | 228.1 GB | 0.2 GB | 0.8 GB | 229.1 GB | 11.4 GB |
| Q8 · near full quality, almost twice Q4 | ~12 pages8,192 tokens | Doesn't fit | 343.8 GB | 0.2 GB | 0.8 GB | 344.8 GB | 11.4 GB |
| FP16 · full quality, over three times Q4 | ~12 pages8,192 tokens | Doesn't fit | 642.6 GB | 0.2 GB | 0.8 GB | 643.6 GB | 11.4 GB |
| Q4 · standard, longer conversation | ~49 pages32,768 tokens | Doesn't fit | 199.7 GBmeasured file | 0.7 GB | 0.8 GB | 201.2 GB | 11.4 GB |
speed: not estimated for MoE yet. This model only uses part of itself for each word (a mixture of experts), and our speed estimate does not handle that yet.
Sources and method
Memory and speed on this page are our estimate, not a measured test. Checked . Unsloth UD-Q4_K_XL GGUF.
Z.ai's open-weight multimodal mixture of experts (MIT licence), the smaller sibling of GLM-5.3. The model card says 320B total parameters with 18B active per token. Hugging Face safetensors metadata lists 321,323,031,390 parameters (F8_E4M3 314.40B, BF16 6.93B, F32 295,518), including the vision encoder and 1 MTP layer, stored here as 321.32B, so our count is an estimate. Config (Glm5NextForConditionalGeneration, text_config glm5_next_text): 45 layers (first 3 dense), hidden size 4096, 288 routed experts plus 1 shared, 8 routed per token, 1 MTP layer, max_position_embeddings 1,048,576. Attention is hybrid: 34 linear-attention layers (KDA delta-rule linear attention, 64 heads x 128) and 11 DeepSeek-style sparse-attention layers (MLA, kv_lora_rank 512, qk_rope_head_dim 0, sparse indexer 32 heads x 128, top-k 2048), every 4th layer from layer 3. Our estimate counts conversation memory only for the 11 sparse-attention layers (latent width 512, K+V formula), 0.18 GB at 8,192 tokens. Linear-attention layers are stored with a sliding window of 0 so our estimate gives them no conversation memory; their fixed recurrent state and the indexer key cache are not counted. Q4 uses the measured Unsloth UD-Q4_K_XL file, 199.71 GB (199,707,321,472 bytes). Q5, Q8 and FP16 are our bytes-per-parameter estimates (0.71 / 1.07 / 2.0 x 321.32B). Unsloth GGUF sizes, shards summed from the Hugging Face tree, decimal GB, checked 29 Oct 2026 (https://huggingface.co/unsloth/GLM-5.3-Flash-GGUF): UD-IQ1_S 93.09, UD-IQ1_M 97.58, UD-IQ2_XXS 101.84, UD-Q2_K_XL 108.72, UD-IQ3_XXS 120.37, UD-Q3_K_XL 147.54, UD-IQ4_XS 156.82, UD-Q4_K_XL 199.71, UD-Q5_K_XL 240.31, UD-Q6_K_XL 291.83, Q8_0 340.98, BF16 641.64; vision mmproj-F16 1.13 GB extra. ggml-org/GLM-5.3-Flash-GGUF: Q4_K 181.37. antirez/glm-5.3-flash-gguf: Q2 96.51, Q4_K 190.88. MLX: mlx-community/GLM-5.3-Flash-4bit 203.99 GB; pipenetwork/GLM-5.3-Flash-MLX-4bit 177.55 GB. NVIDIA NVFP4 checkpoint 204.44 GB. Official FP8 weights are 328.34 GB. Pruned derivatives are not GLM-5.3-Flash and are not scored. Mainline llama.cpp added GLM-5.3-Flash support in pull request 27773, merged 30 September 2026 (https://github.com/ggml-org/llama.cpp/pull/27773). Unsloth says the 1-bit quant works on 100 GB RAM and 3-bit on 128 GB devices (https://unsloth.ai/docs/models/glm-5.3-flash); that is a vendor claim, not tested by us. On our rule a 256 GB Mac Studio M5 Ultra (192 GB usable) holds UD-IQ4_XS (157.80 GB with context) but not UD-Q4_K_XL (200.69 GB); a 128 GB box (96 GB usable) holds only UD-IQ1_S (94.07 GB). Released 26 August 2026 (Artificial Analysis); Hugging Face repo created 25 August 2026. Artificial Analysis Intelligence Index v5.0: not scored yet (checked 29 Oct 2026). Thinking modes low / high / max. Ollama lists only a cloud tag (glm-5.3-flash:cloud), checked 29 Oct 2026.
Questions
- Can I run GLM-5.3-Flash on an RTX 3060 12GB?
- No: it needs about 201 GB and this card has 12 GB of graphics memory.
- How much memory does GLM-5.3-Flash need?
- About 200.7 GB at the standard quality (Q4) for a conversation of up to about 12 pages of text (8,192 tokens), by our estimate: 199.7 GB for the model file (measured), 0.2 GB to hold the conversation and 0.8 GB of working space.
- How fast is GLM-5.3-Flash on an RTX 3060 12GB?
- We don't give a speed for this model yet. This model only uses part of itself for each word (a mixture of experts), and our speed estimate does not handle that yet.
Speeds and memory are our estimate from the hardware specs, not a measured test. Prices are dated and may change. How we calculate