deploybase

A100 40GB vs 80GB: Specs, Benchmarks & Cloud Pricing Compared

Deploybase · January 15, 2025 · GPU Comparison

Contents

A100 40GB vs 80GB: Overview

The A100 40GB vs 80GB comparison reveals critical differences in memory bandwidth, batch processing capacity, and real-world inference costs. Both use NVIDIA's Ampere architecture with identical compute cores (6,912 CUDA cores across 108 SMs), but the 80GB variant doubles the high-bandwidth memory, creating distinct performance profiles for different workloads. This distinction matters enormously when selecting GPU infrastructure on platforms like RunPod ($1.19-$1.39/hour) or Lambda ($1.48/hour).

The fundamental question: does the model fit comfortably with margin, or do developers need every last byte of VRAM? That answer determines whether the 40GB suffices or whether the 80GB justifies the cost premium.

Architecture and Memory Differences

Memory Configuration

The A100 40GB uses HBM2e (High Bandwidth Memory 2e) with a 5,120-bit bus, delivering 1.555 TB/s peak bandwidth (PCIe). The 80GB model uses the same HBM2e technology but doubles the stacks, increasing total bandwidth. Here's what that means in practice:

A100 40GB: 40 GB total, organized as 20 channels at 2GB each A100 80GB: 80 GB total, organized as 20 channels at 4GB each

The memory subsystem operates identically otherwise. Both have the same L2 cache (40 MB), the same number of memory controllers (20), and the same error correction (SECDED ECC). What changes is pure capacity.

Compute Characteristics

Both SKUs contain 6,912 CUDA cores organized into 108 SMs (Streaming Multiprocessors). Each SM has 64 FP32 cores, 64 INT32 cores, 32 FP64 cores, 4 Tensor cores for sparsity, and 2 NVLink 3.0 ports (for 8-GPU configurations). Peak FP32 performance: 19.5 TFLOPS for either variant. Peak Tensor core throughput (FP16/BF16): 312 TFLOPS on both.

The compute ceiling is identical. Memory is what separates them.

Clock Speeds

Both ship at the same boost clock (1.41 GHz nominal, up to 1.87 GHz under thermal headroom). Both support the same Tensor core data types: TF32, BF16, and FP16. Power consumption: 400W TDP (SXM) or 300W (PCIe 80GB) / 250W (PCIe 40GB), though multi-GPU configurations can draw significantly more.

Performance Benchmarks

MLPerf Inference Results (Q2 2024)

The 40GB and 80GB perform identically on single-instance benchmarks: ResNet-50 achieves 4,200 images/second on either. BERT large (batch size 8) runs at 95 samples/second on both. The distinction emerges with larger batches and higher concurrency.

Batch Size Scaling

LLaMA 70B (FP16) requires approximately 140GB unquantized. This exceeds even the 80GB limit. However, with bfloat16 (43.75GB) or 8-bit quantization (18.75GB), both A100 SKUs handle this model. The 40GB version leaves marginal headroom: roughly 4GB for activations and batch processing on a single instance. The 80GB variant allows 36GB breathing room.

Batch size impact (LLaMA 13B, FP16, measured throughput):

40GB A100:

  • Batch size 16: 182 tokens/second
  • Batch size 32: 198 tokens/second
  • Batch size 64: 142 tokens/second (memory pressure)

80GB A100:

  • Batch size 16: 182 tokens/second
  • Batch size 32: 198 tokens/second
  • Batch size 64: 198 tokens/second
  • Batch size 128: 156 tokens/second

The 80GB maintains throughput at higher concurrency. The 40GB begins performance cliff at batch 64.

Fine-Tuning Performance

For training workloads, the memory equation reverses. ResNet-50 training (batch size 1,024) on 8-GPU clusters requires roughly 160GB aggregate memory to hold gradients, optimizer states, and activations. Eight 40GB A100s provide 320GB (adequate). Eight 80GB A100s provide 640GB (excessive for this workload).

However, full-parameter fine-tuning of Mistral 8x7B MoE (121B parameters) requires roughly 250GB per GPU when using 16-bit precision. The 80GB variant necessitates gradient checkpointing or model parallelism. The 40GB variant cannot fit this model at all.

Bert large fine-tuning (batch size 32, sequence length 512):

  • 40GB A100: 42 seconds per epoch
  • 80GB A100: 42 seconds per epoch

Identical. Memory pressure doesn't manifest until developers hit the capacity limit.

Real-World Inference Scenarios

Deployment scenarios reveal the practical gap. An API serving Claude Sonnet 4.6 level models (typically 50-70B parameters quantized) through vLLM:

40GB A100 deployment:

  • Sustainable concurrent users: 8-12 at p99 latency <200ms
  • Memory utilization: 38-39 GB
  • Zero-copy optimization possible: no

80GB A100 deployment:

  • Sustainable concurrent users: 16-24 at p99 latency <200ms
  • Memory utilization: 48-56 GB
  • Zero-copy optimization possible: yes

The difference is serving capacity, not raw speed per request.

Cloud Provider Pricing

RunPod Pricing (March 2026)

RunPod lists A100 SKUs at:

  • A100 PCIe 40GB: $1.19/hour
  • A100 PCIe 80GB: $1.39/hour
  • A100 SXM 40GB: unavailable
  • A100 SXM 80GB: $1.39/hour

The 40GB carries a $0.20/hour discount relative to 80GB. Over a month of 730 hours continuous usage (rare), that's $146 savings. However, most deployments don't run continuously. For a 1,000-hour monthly usage pattern (typical for dev/test):

  • A100 40GB: $1,190
  • A100 80GB: $1,390
  • Difference: $200/month or 16.8% premium

This premium exists because RunPod allocates 80GB instances to a tighter availability pool.

Lambda Labs Pricing (March 2026)

Lambda offers A100 at $1.48/hour, with both 40GB and 80GB carrying identical pricing:

  • A100 PCIe 40GB: $1.48/hour
  • A100 PCIe 80GB: $1.48/hour

This represents a strategic pricing decision: Lambda prioritizes developer accessibility and doesn't segment by memory tier. At 1,000 monthly hours:

  • Either SKU: $1,480

This eliminates the capacity vs. cost trade-off for Lambda customers.

Broader Market Context

CoreWeave (8-GPU clusters, excluding pricing in this comparison) typically charges per-instance regardless of memory tier within a generation. The market trend moves toward capacity-based pricing (per-GB) rather than SKU-based pricing, gradually eroding the 40GB vs. 80GB price distinction.

When to Choose 40GB

The 40GB A100 makes sense in specific scenarios:

Small Model Serving: Deploying models under 30B parameters (quantized) doesn't stress 40GB capacity. Mistral 7B, Llama 2 13B, or smaller specialized models fit comfortably with ample headroom for batching.

Research and Experimentation: Development environments can tolerate the occasional OOM event. Swap space and CPU fallback provide safety nets. Development hours carry lower cost tha production inference downtime.

Cost-Constrained Inference: If serving a model that fits in 40GB, the 16.8% price reduction on RunPod compounds over months. Multiply across 10-20 instances, and developers're looking at $2,000-$4,000 monthly savings without performance loss.

Single-User or Low-Concurrency APIs: A chatbot serving one user at a time, or batch processing during off-peak hours, never stresses the memory bandwidth. Utilization stays under 30GB routinely.

Distributed Inference: When load-balancing across 4-8 instances (via vLLM round-robin or similar), no single instance needs to handle peak concurrency. The distributed footprint absorbs variance.

These scenarios require discipline. Monitor actual memory utilization; if developers're touching 39GB regularly, the 40GB variant is the wrong choice.

When to Choose 80GB

The 80GB A100 justifies its cost in these situations:

Large Model Inference: Deploying Llama 2 70B, Mistral 8x7B, or larger models quantized requires 40-60GB VRAM alone, leaving minimal room for batching. The 80GB variant provides 20-40GB for concurrent requests.

High-Concurrency Serving: An API handling 20+ concurrent users needs memory headroom. Each concurrent request in vLLM reserves KV cache proportional to context length. A 32K context window per user consumes significant memory; 80GB allows higher concurrency without performance cliffs.

Fine-Tuning Production Models: Full-parameter fine-tuning of 70B-class models requires 60-75GB per GPU when using bfloat16 and gradient accumulation. The 80GB variant leaves room to adjust batch size and gradient steps without triggering OOM.

Multi-Model Deployments: Simultaneously loading 2-3 different models on a single GPU (model switching overhead is lower than orchestrating multiple instances) requires ~50GB aggregate. The 80GB variant handles this; the 40GB does not.

LoRA Adapter Serving: While LoRA reduces per-adapter memory (typically 10-50MB), serving 50+ adapters simultaneously alongside the base model touches 40GB easily. The 80GB variant accommodates growth without redeployment.

The decision hinges on concurrency and model size. If either is large, 80GB is non-negotiable.

TCO Analysis

A 12-month total cost of ownership comparison for an API serving Llama 2 70B with target concurrency of 16 simultaneous users:

40GB A100 Approach

Hardware setup: 2x A100 40GB on RunPod @ $1.19/hour Monthly compute cost (1,460 hours/month on-demand): $1,457 × 2 = $2,914 Memory pressure: Peak concurrency saturates one instance; second instance handles burst Performance: p99 latency 320ms at peak; p50 latency 120ms average 12-month cost: $34,968

Trade-offs: occasional memory exhaustion, queue buildup during spikes, customer complaints during traffic surges.

80GB A100 Approach

Hardware setup: 1x A100 80GB on Lambda @ $1.48/hour Monthly compute cost (1,460 hours/month on-demand): $1,460 × 1 = $1,460 Memory overhead: Peak concurrency uses 62GB; 18GB buffer remaining Performance: p99 latency 140ms at peak; p50 latency 110ms average 12-month cost: $17,520

Trade-offs: higher per-hour cost, but fewer instances reduce operational overhead (one autoscaling group instead of two, simpler monitoring).

Verdict: The 80GB approach costs 50% less for this workload. The per-hour premium is offset by requiring fewer instances.

However, if serving Mistral 7B (fits easily in 40GB with concurrency):

40GB Approach (Mistral 7B)

Hardware setup: 1x A100 40GB on RunPod @ $1.19/hour Monthly compute cost: $1,460 × $1.19 = $1,737 Performance: p99 latency 50ms; p50 latency 35ms average 12-month cost: $20,844

80GB Approach (Mistral 7B)

Hardware setup: 1x A100 80GB on Lambda @ $1.48/hour Monthly compute cost: $1,460 × $1.48 = $2,161 Performance: p99 latency 48ms; p50 latency 33ms average 12-month cost: $25,932

Verdict: For smaller models, 40GB saves $5,088 annually with negligible performance difference.

The 12-month TCO hinges entirely on whether the workload requires 80GB capacity. If it does, the cost savings from consolidation overwhelm the per-hour premium. If it doesn't, the 40GB premium is unjustifiable.

Memory Bandwidth Saturation and Cache Hierarchy

The A100 40GB PCIe achieves 1.555 TB/s bandwidth; the A100 80GB PCIe achieves 1.935 TB/s; and the A100 80GB SXM achieves 2.0 TB/s. The practical throughput ceiling also depends on cache efficiency and kernel launch latency, with realistic utilization at 30-60% of peak.

A100 memory hierarchy:

  • L1 cache: 192 KB per SM (12 MB aggregate)
  • L2 cache: 40 MB shared
  • HBM2e: 1.555–2.0 TB/s peak (variant-dependent)

The 80GB variant doesn't increase bandwidth; it increases capacity for working set reuse. If the training loop repeatedly accesses the same 35GB of parameters and activations, the 40GB hits capacity limits. Spilling to CPU memory destroys performance (300x slower than HBM).

This distinction is crucial for large batch training. Batch size 256 on ResNet-50 fits comfortably in 40GB (requires ~36GB). Batch size 512 exceeds capacity; the 40GB variant cannot achieve full utilization without memory management tricks.

Inference vs. Training Trade-offs

Inference workloads typically care about memory capacity; training workloads care about memory and compute balance.

For inference:

  • Model weights + KV cache dominate
  • Activation memory minimal (single-token generation)
  • 40GB sufficient for most production models (60B and below, quantized)
  • 80GB required for very large models or high concurrency

For training:

  • Model weights + gradients + optimizer states + activations + KV cache (causal LLM training)
  • Aggregate memory requirement: 2-4x model parameter count
  • 40GB ceiling: 10B parameter models with batch size 32
  • 80GB ceiling: 20-30B parameter models with batch size 32

Few teams train 70B models on single GPUs; distributed training spreads the load. This shifts the training workload comparison toward "40GB is sufficient if you parallelize correctly."

Market Context and Deprecation Risk

The A100 entered production in 2020. As of March 2026, it's the legacy GPU in NVIDIA's lineup. H100 (2023) and H200 (2024) dominate new deployments. Why:

  • Availability: 40GB A100s are increasingly scarce; cloud providers deprecate PCIe variants first
  • Price erosion: A100 pricing declines 8-12% annually as newer generations launch
  • Supply constraints: If developers need A100s, availability matters more than SKU choice

For new projects, consider H100 instead. The comparison shifts to H100 80GB ($2.69/hour on RunPod) vs. A100 80GB ($1.39/hour). The H100 costs roughly 2x per-hour but delivers 2-3x inference throughput, breaking even on per-token cost.

The A100 vs H100 comparison explores the generational upgrade. See the GPU pricing comparison for all A100 variants, including the full 80GB SXM variant and PCIe options. Pricing dynamics are covered in the A100 pricing guide.

FAQ

Can I swap 40GB A100s and 80GB A100s in the same cluster?

Technically yes, but operationally no. A vLLM cluster scheduler treats them as separate resource classes. If you're auto-scaling and one instance type fills before another, you'll face uneven utilization and higher per-token costs.

What's the power efficiency difference?

The SXM variants carry a 400W TDP; PCIe variants are 300W (80GB) and 250W (40GB). The 80GB SXM draws the same power as the 40GB SXM. For the PCIe 80GB variant, power is 300W versus 250W for the 40GB — a modest increase for significantly more memory.

Should I choose based on price alone?

Never. Price matters only insofar as the instance fits your workload. An undersized 40GB that hits memory limits continuously will outspend an 80GB instance through degraded performance, failed requests, and operational overhead.

How does this compare to renting CPU memory?

CPU memory costs roughly $0.15 per GB per month on commodity cloud platforms. Spilling a GPU workload to 20GB of CPU memory adds $3/month in memory cost but destroys performance (300x slowdown). Don't spill; choose the right GPU size upfront.

Can I use mixed precision to squeeze more into 40GB?

Yes. FP8 (8-bit float) quantization halves model size but requires careful calibration. Most production systems achieve better results with bfloat16 (no quantization artifacts) and sufficient 80GB capacity instead.

What's the inter-GPU bandwidth within an 8-GPU A100 cluster?

NVLink 3.0 offers 600 GB/s total bidirectional bandwidth per GPU. An A100 with two NVLink ports sustains 600 GB/s GPU-to-GPU within a node. This is substantially lower than the HBM bandwidth (1.555–2.0 TB/s depending on variant), but still enables efficient intra-node collective operations (all-reduce for distributed training).

Does the 40GB A100 PCIe perform differently from the SXM?

PCIe variants connect via PCI Express 4.0 (64 GB/s), while SXM variants use a direct socket connection (no bandwidth reduction). On single-instance workloads, this doesn't manifest. On multi-GPU clusters, PCIe introduces contention on the host's PCI bus. For >2 GPU clusters, SXM is standard; PCIe is cost-optimized for smaller setups.

Sources

  • NVIDIA. "NVIDIA A100 Tensor Core GPU Architecture." March 2020. Retrieved from official NVIDIA documentation.
  • NVIDIA. "Ampere GA-102 GPU Architecture Whitepaper v2." 2021. Retrieved from official NVIDIA whitepaper repository.
  • RunPod. "GPU Pricing." Accessed March 2026. Retrieved from runpod.io/pricing.
  • Lambda Labs. "GPU Cloud Pricing." Accessed March 2026. Retrieved from lambdalabs.com/service/gpu-cloud.
  • DeployBase. "GPU Benchmark Database." March 2026. Internal research dataset.