A10G vs L4
Specs, price, and performance compared
Specs
▲ indicates the higher value for that spec. TDP: lower is better for density.
A10G vs L4: Workload Fit
The A10G is built on the GA102 architecture (Ampere, Samsung 8nm), while the L4 uses AD104 (Ada Lovelace, TSMC 4N). The A10G delivers 125 TFLOPS of FP16/BF16 throughput — 2.1× the 60.6 TFLOPS of the L4. Neither GPU supports FP8 precision.
Memory capacity and bandwidth are the primary constraints for large language model workloads. The A10G provides 24GB of GDDR6 — the same capacity as the L4, though in GDDR6. Memory bandwidth favors the A10G at 600 GB/s — 2.0× the 300 GB/s of the L4. Neither GPU supports NVLink — each card is an independent VRAM island.
Power consumption shapes rack density and operating cost. The L4 draws 72W TDP versus 150W for the A10G. On a TFLOPS-per-watt basis, the L4 is the more efficient option, delivering 61 TFLOPS at 72W.
The A10G is best suited for Inference serving, Graphics workloads, Low-power deployments. The L4 targets Efficient inference, Edge deployments, Low-power serving. For high-density inference racks where power budget is the constraint, the L4's 72W envelope allows more cards per rack than the A10G's 150W. For throughput-bound inference, the A10G's higher TFLOPS ceiling gives it an advantage when the model fits in either card's VRAM.
Workload Cost Comparison
Total job cost = observed median $/hr × GPU count × hours. Spot pricing typically 40–70% lower.
The A10G costs 1.1× more than the L4 at current observed median prices. Memory bandwidth is 2.0× higher on the A10G (600 GB/s vs 300 GB/s), which directly improves token throughput for memory-bound LLM inference. The A10G delivers 2.1× the FP16 compute of the L4 (125 vs 60.6 TFLOPS), supporting faster training iteration and higher batch throughput.
Based on observed on-demand median prices. Actual cost depends on provider, region, and availability.
Strengths & Weaknesses
- Higher memory bandwidth (600 GB/s vs 300 GB/s on the L4)
- 2.1× the FP16 compute throughput of the L4 (125 vs 60.6 TFLOPS)
- Higher power draw at 150W vs 72W on the L4
- Lower power draw at 72W vs 150W — fits more cards per rack
- Newer architecture (AD104, 2023) vs GA102 (2021)
- Lower memory bandwidth than the A10G (300 GB/s vs 600 GB/s)
- Lower FP16 throughput than the A10G (60.6 vs 125 TFLOPS)
- Inference serving
- Graphics workloads
- Low-power deployments
- Only 24GB GDDR6 — limits to ~13B models at FP16
- No NVLink — single-card VRAM ceiling
- Lower memory bandwidth than HBM-based alternatives
- Efficient inference
- Edge deployments
- Low-power serving
- Only 24GB VRAM — limits to ~13B models at FP16
- Low memory bandwidth (300 GB/s) — bottleneck for large batches
- No NVLink — single-card only