Last updated: August 2026
For most 2026 workloads the split is simple: pick the A100 40GB for LoRA and QLoRA fine-tuning and quantized inference on models up to about 13B parameters, and pick the A100 80GB to serve 13B to 70B models, run context lengths past 32K, or hold full precision without quantization. Both cards ship the same 6,912 CUDA cores and the same 19.5 TFLOPS of FP32 compute. The only differences that decide the pick are memory capacity, memory bandwidth, and MIG slice size.
That decision matters more this month. NVIDIA’s Vera Rubin generation launched in early August 2026, and as Blackwell volume ramps, the A100 is settling into its role as the mainstream inference and fine-tuning card rather than a frontier trainer. It is not winding down: NVIDIA’s Jensen Huang has called the A100 fleet mission-capable from 2020 through 2029, and A100 supply is still active across cloud providers. So for teams renting one now, the live question is which variant. For full specs, benchmarks, and rental costs, see the NVIDIA A100 GPU guide; this post is the deeper 40GB-versus-80GB decision.
TL;DR
- Same compute, different memory. Both variants have 6,912 CUDA cores, 432 third-gen Tensor Cores, and 19.5 TFLOPS FP32. The 80GB doubles capacity (40GB to 80GB) and adds bandwidth.
- Bandwidth gap is ~31% on SXM. The 40GB runs HBM2 at 1,555 GB/s; the 80GB runs HBM2e at 1,935 GB/s (PCIe) or 2,039 GB/s (SXM). That gap is what wins memory-bandwidth-bound decode.
- 40GB is the fine-tuning card. Best for LoRA/QLoRA on 7B to 13B models and quantized inference. It splits into 7 MIG instances of 5GB each.
- 80GB is the serving card. Best for 13B to 65B models, 70B across 2 cards, long context, and multi-tenant serving via 7 MIG instances of 10GB each.
- Rule of thumb: confirm your model, batch size, and context length fit under 40GB before choosing it; when any of the three pushes past 40GB, the 80GB is the card.
What is the actual difference between the A100 40GB and 80GB?
The A100 40GB and 80GB are the same silicon with different memory. Both are built on NVIDIA’s Ampere architecture with 6,912 CUDA cores, 432 third-generation Tensor Cores, 108 streaming multiprocessors, 40MB of L2 cache, and 54 billion transistors on a 7nm process, per NVIDIA’s datasheet. Peak FP32 is 19.5 TFLOPS on either card, and Tensor Core FP16/BF16 reaches 312 TFLOPS.
What changes is memory. The 40GB variant carries 40GB of HBM2, and the 80GB variant carries 80GB of the faster HBM2e. HBM stands for High Bandwidth Memory, the stacked DRAM that sits next to the GPU die. Doubling the stacks doubles capacity and raises total bandwidth, and it is the entire basis for choosing one over the other.
How big is the memory bandwidth gap between A100 40GB and 80GB?
The 80GB moves data faster, and on the SXM form factor the gap is about 31%. NVIDIA rates the 40GB at 1,555 GB/s on both PCIe and SXM, the 80GB PCIe at 1,935 GB/s, and the 80GB SXM at 2,039 GB/s (roughly 2.0 TB/s). Bandwidth is measured in gigabytes per second (GB/s), and it caps how fast the GPU can read model weights during token generation.
That gap decides memory-bandwidth-bound work. In the decode phase of LLM inference, every generated token requires reading the entire weight tensor once, so higher bandwidth directly shortens time-per-token, which is why the 80GB is the pick when memory bandwidth limits throughput, including long-context inference past 32K tokens. For compute-bound training that fits in 40GB, the two cards perform almost identically, because their CUDA and Tensor Core counts are the same.
A100 40GB vs 80GB: full spec comparison
| Spec | A100 40GB | A100 80GB |
|---|---|---|
| GPU memory (GB) | 40 GB HBM2 | 80 GB HBM2e |
| Memory bandwidth (GB/s), PCIe | 1,555 | 1,935 |
| Memory bandwidth (GB/s), SXM | 1,555 | 2,039 |
| CUDA cores | 6,912 | 6,912 |
| Tensor Cores (3rd gen) | 432 | 432 |
| FP32 (TFLOPS) | 19.5 | 19.5 |
| FP16/BF16 Tensor (TFLOPS) | 312 | 312 |
| MIG instances | Up to 7 @ 5 GB | Up to 7 @ 10 GB |
| Max TDP (W), PCIe / SXM | 250 / 400 | 300 / 400 |
| NVLink bridge (2 GPUs) | 600 GB/s | 600 GB/s |
Takeaway: the 80GB doubles usable memory and adds up to 31% more bandwidth, while compute (CUDA cores, Tensor Cores, FP32/FP16 throughput) is identical between the two variants. Source: NVIDIA A100 datasheet.
Which models fit on the A100 40GB vs the 80GB?
The 40GB comfortably handles models up to roughly 13B parameters; the 80GB extends that to the 65B range on a single card and 70B across two. The A100 40GB is the standard card for LoRA and QLoRA fine-tuning of 7B to 13B models and for inference on quantized LLMs, which covers a large share of real production work. LoRA and QLoRA are parameter-efficient fine-tuning methods that update a small adapter instead of the full model, so they fit in far less VRAM than full fine-tuning.
If you’d rather skip the manual setup, Razer AIKit packages vLLM, Jupyter Lab, and a web UI into a single one-click GPU container, letting you fine-tune or serve any of the 300,000+ Hugging Face models on an A100 without configuring the stack yourself.
The 80GB is the card you pick when memory is the constraint. It handles 13B to 65B models on one card, and serving a 70B model in full FP16 precision means two 80GB cards with tensor parallelism, since a single 80GB is not enough. A single A100 40GB cannot run a 70B model in full precision at all and would need four cards in BF16 or heavy quantization, which is usually the moment teams step up to the 80GB or to an H100. For a hands-on single-card example, see running a model on one A100 with vLLM.
| Workload | A100 40GB | A100 80GB |
|---|---|---|
| LoRA/QLoRA fine-tune, 7B to 13B | Yes | Yes |
| Quantized inference, up to 13B | Yes | Yes |
| Full-precision inference, 13B to 65B | Tight to no | Yes |
| Serve 70B (FP16) | No (needs 4x BF16 or quant) | 2x with tensor parallelism |
| Long context beyond 32K tokens | Limited by bandwidth | Preferred |
| Multi-tenant serving (MIG) | 7 slices @ 5 GB | 7 slices @ 10 GB |
Takeaway: choose the 40GB for fine-tuning and small-to-mid inference, and the 80GB whenever the model, the batch size, or the context length pushes past what 40GB holds.
How does MIG differ between the two A100 variants?
Both cards support MIG, but the 80GB gives each slice twice the memory. MIG (Multi-Instance GPU) partitions one physical A100 into up to seven isolated instances that each appear to the operating system as a separate GPU. On the 40GB, each of the 7 instances gets 5GB; on the 80GB, each gets 10GB.
That per-slice difference is the quiet reason the 80GB wins in multi-tenant serving. A 10GB MIG slice can host a 7B model comfortably, letting one 80GB card serve seven small models or seven teams at a fraction of the hardware of seven separate GPUs, while a 5GB slice on the 40GB is tight for anything but the smallest quantized models. If your goal is dense, isolated multi-model inference, the 80GB’s larger slices change what fits.
Should you still choose an A100 in 2026 now that Blackwell and Rubin are out?
Yes, for inference and mid-size fine-tuning the A100 is the mainstream pick, and the newer silicon reinforces that. NVIDIA’s Rubin launch and the Blackwell ramp are moving previous-generation cards down the stack, and as hyperscalers and labs refresh fleets for Blackwell-generation hardware, more A100-class capacity is entering secondary and marketplace supply. That growing availability is one reason the A100 stays the default for teams that do not need frontier-scale compute.
The A100 keeps a role because inference and fine-tuning tolerate older architectures well. Its mature CUDA and driver stack still receive support, MIG partitioning makes it efficient for multi-tenant serving, and demand held steady enough that A100 rental contracts run through 2029. Reserve Rubin, Blackwell, and H100 for large-scale training and latency-sensitive production; reach for the A100 40GB for fine-tuning and the 80GB for cost-efficient serving. For where the A100 sits against the newest chips, see NVIDIA B300 vs B200 vs H200.
FAQ
Is the A100 80GB worth choosing over the 40GB?+
It depends on your model size. If you run LoRA/QLoRA fine-tuning or quantized inference up to 13B parameters, the 40GB is sufficient. If you serve 13B to 70B models, use context lengths past 32K, or need full precision without quantization, the 80GB’s doubled capacity and up to 31% more bandwidth make it the right card.
What is the bandwidth difference between the A100 40GB and 80GB?+
The 40GB uses HBM2 rated at 1,555 GB/s. The 80GB uses faster HBM2e at 1,935 GB/s on PCIe and 2,039 GB/s (about 2.0 TB/s) on SXM. On the SXM form factor that is roughly a 31% bandwidth increase, which matters most for memory-bandwidth-bound decode and long-context inference, not for compute-bound training.
Can the A100 40GB run a 70B model?+
Not in full precision. A single A100 40GB cannot hold a 70B model in FP16 and would need four cards in BF16 or aggressive quantization. Serving 70B in full precision typically means two A100 80GB cards with tensor parallelism, or stepping up to an H100. For 70B work, start with the 80GB.
Does the A100 40GB use HBM2 or HBM2e?+
NVIDIA’s datasheet lists the 40GB with HBM2 at 1,555 GB/s and the 80GB with the newer HBM2e at up to 2,039 GB/s. Some third-party pages describe both as HBM2e, but the official specification distinguishes them, and the practical difference is the capacity and bandwidth shown on the datasheet.
How many MIG instances can each A100 variant split into?+
Both split into up to seven Multi-Instance GPU (MIG) partitions. On the 40GB each instance gets 5GB of memory; on the 80GB each gets 10GB. The larger 10GB slices let one 80GB card host up to seven 7B-class models at once, which is why the 80GB is preferred for dense multi-tenant serving.
Which A100 variant is best for LoRA fine-tuning?+
The A100 40GB is the standard card for LoRA and QLoRA fine-tuning of 7B to 13B models. Step up to the 80GB only if your base model, sequence length, or batch size exceeds 40GB, or if you want to fine-tune larger models without optimizer offloading.
Is there a performance difference between the two A100 variants on training that fits in 40GB?+
Almost none. Both cards have identical CUDA and Tensor Core counts and the same 19.5 TFLOPS FP32 and 312 TFLOPS FP16/BF16 throughput. For a compute-bound job that fits inside 40GB, the two perform the same; the 80GB only pulls ahead when the workload is memory-capacity or memory-bandwidth bound.
