How much VRAM does GTE Embeddings Large 335M need?
GTE Embeddings Large 335M needs about 0.9 GB of VRAM at Q4_K_M, or 1.1 GB at Q8_0 and 1.4 GB at FP16. That fits a consumer card: the smallest in our set that holds it at Q4_K_M is the RTX 3060 12GB (12 GB). Weights plus runtime overhead; with only 1K of context the cache stays small.
Weights-plus-runtime footprint across the six most common quantizations. KV cache is context-dependent and comes on top. A 1K window keeps it modest here, but it still comes out of the same budget; size it in the GPU Memory Calculator.
Can your GPU run GTE Embeddings Large 335M?
Pick your card — the answer below is computed by the same engine that produces every figure on this page.
RTX 3060 12GB runs GTE Embeddings Large 335M at Q4_K_M with 10.5 GB to spare; plenty of room for a long context window or a second model alongside it.
Weights plus runtime overhead. KV cache is not included — it depends on context length and this model’s published architecture.
| Weights (Q4_K_M) | 0.2 GB |
| Runtime overhead | 0.8 GB |
| Estimated total | 0.9 GB |
| Usable VRAM (95% of 12 GB) | 11.4 GB |
Across every GPU we track
- RTX 3060 12GBExcellent+10.5 GB
- RTX 4060 Ti 16GBExcellent+14.3 GB
- RTX 4070 Ti SUPERExcellent+14.3 GB
- RTX 3090Excellent+21.9 GB
- RTX 4090Excellent+21.9 GB
- RTX 5090Excellent+29.5 GB
- A100 40GBExcellent+37.1 GB
- RTX A6000Excellent+44.7 GB
- L40SExcellent+44.7 GB
- A100 80GBExcellent+75.1 GB
- H100 80GBExcellent+75.1 GB
- H200 141GBExcellent+133.0 GB
Estimates, not guarantees: real usage moves with runtime, driver, batch size and context. Size a specific context window in the GPU Memory Calculator.
How was this number calculated? Every figure here comes from Bitpute’s documented calculation methodology — parameters, precision, quantization, runtime overhead and usable VRAM, each formula written out in full.
Memory by quantization
| Quant | Weights | + runtime overhead |
|---|---|---|
| FP16 | 0.6 GB | 1.4 GB |
| Q8_0 | 0.3 GB | 1.1 GB |
| Q6_K | 0.3 GB | 1.0 GB |
| Q5_K_M | 0.2 GB | 1.0 GB |
| Q4_K_M | 0.2 GB | 0.9 GB |
| Q4_0 | 0.2 GB | 0.9 GB |
Overhead = 0.75 GB + 5% of weights (CUDA context, buffers). Add KV cache on top. A 1K window keeps it modest, but it shares the same budget.
Which quantization should you actually run?
This is an embedding model, not a chat LLM — the weights are well under a gigabyte, so it runs comfortably on CPU or any GPU. Quantization and GPU fit are not real constraints here; batch size and throughput matter far more than VRAM.
Single-GPU fit at Q4_K_M (0.9 GB + KV)
RTX 3060 12GBRTX 4060 Ti 16GBRTX 4070 Ti SUPERRTX 3090RTX 4090RTX 5090A100 40GBRTX A6000L40SA100 80GBH100 80GBH200 141GB
Where the memory goes
Weights and overhead are exact for Q4_K_M. The KV bar is a generic 32-layer transformer at 8K — on a model this small it is the term that decides whether you fit, so check yours in the calculator is for.
Weighing the cheapest card that fits against the next one up? RTX 3060 12GB vs RTX 4060 Ti 16GB compares them on bandwidth, power and which models each one holds.
Common questions
How much VRAM does GTE Embeddings Large 335M need?
GTE Embeddings Large 335M needs about 0.9 GB of VRAM at Q4_K_M, 1.1 GB at Q8_0, or 1.4 GB at FP16. That is weights plus about 0.75 GB of runtime overhead and 5% of weight size; KV cache is additional and depends on context length.
What GPU can run GTE Embeddings Large 335M?
At Q4_K_M the smallest card in our set that fits is the RTX 3060 12GB (12 GB usable at 95%). At FP16 you need the RTX 3060 12GB (12 GB) or larger.
Can GTE Embeddings Large 335M run on 24 GB?
Yes. At Q4_K_M it needs about 0.9 GB, which fits inside the ~22.8 GB usable on a 24 GB card.
More in the GTE Embeddings family
GTE Embeddings Small 33MGTE Embeddings Base 109MGTE Embeddings Qwen2 1.5B InstructGTE Embeddings Qwen2 7B Instruct