NVIDIA A100 80GB SXM

NVIDIA · A100 80GB SXM HBM2e · server-gpu

Marketing label: 80GB HBM2e.

Capacity used for estimates: 80.00 GiB (85.90 GB).

Default system/display reserve (adjustable): 2.00 GiB.

Vendor-marketed GB for discrete GPU VRAM is treated as GiB (2^30 bytes), matching physical GDDR/HBM module organization. This is an explicit unit assumption.

Memory kind: discrete-vram. Software ecosystem notes: CUDA, vllm.

Catalog models at 8,192 cached tokens

Using each model’s GGUF Q4_K_M file when present, otherwise BF16 files. Concurrent sequences = 1. This is a memory screen, not a performance ranking.

Model	Quant	Status	Estimate
Qwen2.5-7B-Instruct	GGUF Q4_K_M	likely-fits	4.92 GiB–5.42 GiB
Qwen2.5-0.5B-Instruct	BF16 safetensors	likely-fits	1.64 GiB–3.52 GiB
Qwen2.5-1.5B-Instruct	BF16 safetensors	likely-fits	3.72 GiB–5.60 GiB
Qwen2.5-3B-Instruct	GGUF Q4_K_M	likely-fits	2.37 GiB–2.80 GiB
Qwen2.5-14B-Instruct	GGUF Q4_K_M	likely-fits	10.00 GiB–10.64 GiB
Qwen2.5-32B-Instruct	GGUF Q4_K_M	likely-fits	20.61 GiB–21.50 GiB
Qwen2.5-72B-Instruct	GGUF Q4_K_M	likely-fits	43.61 GiB–44.98 GiB
Qwen3-1.7B	BF16 safetensors	likely-fits	5.28 GiB–7.19 GiB
Qwen3-4B	GGUF Q4_K_M	likely-fits	3.58 GiB–4.08 GiB
Qwen3-8B	GGUF Q4_K_M	likely-fits	5.93 GiB–6.48 GiB
Qwen3-14B	GGUF Q4_K_M	likely-fits	9.76 GiB–10.39 GiB
Qwen3-32B	GGUF Q4_K_M	likely-fits	20.53 GiB–21.41 GiB
Qwen3-30B-A3B	GGUF Q4_K_M	likely-fits	18.16 GiB–18.93 GiB
Phi-3.5-mini-instruct	BF16 safetensors	likely-fits	10.74 GiB–12.71 GiB
Mistral-7B-Instruct-v0.3	BF16 safetensors	likely-fits	15.13 GiB–17.03 GiB
DeepSeek-R1-Distill-Llama-8B	BF16 safetensors	likely-fits	16.58 GiB–18.49 GiB
DeepSeek-R1-Distill-Qwen-32B	BF16 safetensors	likely-fits	63.65 GiB–65.59 GiB

Sources

NVIDIA A100 product page — GPU Memory 80GB HBM2e (SXM)

Calculator

Pick a device and workload, then see which catalog models may fit.

ModelHardwareQuantizationContext length (cached tokens)Concurrent sequences

Advanced settings

KV cache typeEngineDeploymentDevice countOverhead scenarioSystem / display reserve

GiB (2^30 bytes)

Budget already excludes system reserveExtra safety marginCustom bits per weight Include heavier prefill workspaceDisplay unit

Custom model structure

Import local config.json

Parsed in the browser. The file is not uploaded.

Public Hugging Face repoRevision

Only huggingface.co, config.json, no token. Private repos are rejected.

Total parametersActive parametersLayersQuery headsKV headsHead dimension

Estimate

Likely fits

Under the current assumptions, the high estimate stays below 90% of the available budget.

Estimated memory range: 4.92 GiB – 5.42 GiB

Weights use selected file metadata. Runtime extras are still estimated.

Weights4.36 GiB

KV cache (raw)0.44 GiB

KV extras (quant metadata / packing)0.00 GiB

Activation / workspace0.06 GiB

Engine reserve0.06 GiB

Safety margin0.00 GiB

Available budget: 78.00 GiB · Remaining after estimate: 72.58 GiB – 73.08 GiB

Software compatibility

Documented support. Enough memory does not mean this engine, quant, and OS will run. Support does not mean it will be fast.

Assumptions that affect this result

Full-resident weight estimates use total parameters, including inactive MoE experts.
Weight bytes come from the selected weight files only, not from every file in the repository.
Download file size is not peak GPU memory. Runtime layout, allocator padding, and KV are extra.
Counted files: qwen2.5-7b-instruct-q4_k_m-00001-of-00002.gguf, qwen2.5-7b-instruct-q4_k_m-00002-of-00002.gguf.
Context budget is the number of cached tokens per sequence (prompt plus reserved generation).
Concurrent sequences share one model replica; weights are not multiplied by sequence count.
KV dtype is independent of weight quantization unless you change it.
Using 4 KV heads, not 28 query heads.
Prefill peak and steady decode occupancy are not the same. The high scenario is closer to prefill/workspace pressure.
Engine reserve is not the same as bytes the model actually uses for weights and KV.
llama.cpp compute buffers grow with context and batch. Values here are user-adjustable scenarios, not measured traces — unless a matching calibration is shown.
Host RAM is not added to discrete GPU VRAM.

Calculation method

Catalog models under this workload

Model	Estimate	Estimated memory range
	Likely fits	4.92 GiB – 5.42 GiB
	Likely fits	1.14 GiB – 1.54 GiB
	Likely fits	3.22 GiB – 3.67 GiB
	Likely fits	2.37 GiB – 2.80 GiB
	Likely fits	10.00 GiB – 10.64 GiB
	Likely fits	20.61 GiB – 21.50 GiB
	Likely fits	43.61 GiB – 44.98 GiB
	Likely fits	4.78 GiB – 5.30 GiB
	Likely fits	3.58 GiB – 4.08 GiB
	Likely fits	5.93 GiB – 6.48 GiB
	Likely fits	9.76 GiB – 10.39 GiB
	Likely fits	20.53 GiB – 21.41 GiB
	Likely fits	18.16 GiB – 18.93 GiB
	Likely fits	10.24 GiB – 10.97 GiB
	Likely fits	14.63 GiB – 15.34 GiB
	Likely fits	16.08 GiB – 16.83 GiB
	Likely fits	63.15 GiB – 64.89 GiB