NVIDIA A10G vs T4: Cheapest GPU for Inference (2026)

NVIDIA's T4 and A10G sit next to each other on every hyperscaler's GPU price list, which is exactly why people cross-shop them. But the T4 launched in 2018 on Turing. The A10G launched in 2021 on Ampere. Three years and a full architecture generation apart, and a lot of comparison pages blur that gap by quoting mismatched precision columns, or by repeating the T4's memory bandwidth as "320GB/s" when NVIDIA's own datasheet says 300GB/s.

AWS charges $1.006/hr for a g5.xlarge (one A10G, 24GB) and $0.526/hr for a g4dn.xlarge (one T4, 16GB), a 1.9x price gap for exactly 2x the memory and 2x the memory bandwidth, according to Vantage's g5.xlarge and g4dn.xlarge pricing pages. This post pulls both cards' official datasheets, matches precision to precision instead of cherry-picking columns, and prices both across five providers so you can see where the T4's lower sticker price actually wins and where it doesn't. If your model doesn't fit on either card, there's a tier up covered later in this post. First, if you're pricing this class of hardware on Spheron, the closest current-generation card in the catalog is L40S, which comes up again in the cost section below.

NVIDIA A10G vs T4: Specs, Memory Bandwidth, and Price Per Hour

The T4 is a 70W, low-profile card built for scale-out inference density. The A10G is a 300W, 2-slot card that AWS commissioned as an Ampere-generation variant of the A10 specifically for EC2 G5 instances. Both show up as "budget" options on every provider's GPU list, which is the entire reason they get cross-shopped instead of compared to what actually replaced them.

Full Spec Table

Source: NVIDIA's T4 datasheet and AWS/NVIDIA's A10G datasheet.

Two things jump out once the columns are matched correctly instead of skimmed. First, raw FP16 Tensor throughput is close: 70 TFLOPS dense on the A10G against 65 TFLOPS mixed-precision on the T4, about a 1.08x difference. Second, neither card has FP8 tensor cores. That's easy to assume the newer Ampere card has and the older Turing card doesn't, but FP8 support didn't arrive until Hopper in 2022, a year after the A10G shipped. Several third-party comparisons quote a "3.9x FP16" gap between these cards by comparing the T4's FP32 column against the A10G's FP16-sparse column. That's not a real precision-matched comparison, and it's not what you'll see in practice.

Why a 2018 Card and a 2021 Card Still Get Cross-Shopped

The honest answer is availability, not architecture. Every generation between Turing and Ampere that would sit closer in age to the T4, like the V100, has largely aged out of hyperscaler catalogs entirely. Meanwhile the newer Ada Lovelace cards (L4, L40S) cost more and aren't stocked everywhere. That leaves the T4 and A10G as the two GPUs almost every provider still has racked and billing by the hour for small-model inference, so teams comparing "cheap GPU options" keep landing on both regardless of the three-year gap between them.

The market has started moving past both anyway. RunPod's current pricing catalog doesn't list A10G, A10, or T4 at all, only L4 and newer. If you're choosing a GPU with no legacy constraint pulling you toward Turing or Ampere specifically, that's worth noting before you commit to either card.

What Actually Fits: 7B and Smaller Models on Each Card

This is where the VRAM gap, not the compute gap, decides the outcome. A 24GB card and a 16GB card handle a 7B-8B language model very differently once you account for KV cache, not just weights.

VRAM Math for 7B-8B Models at FP16, INT8, and INT4

An 8B model like Llama 3.1 8B or Qwen2.5-7B needs roughly 2 bytes per parameter at FP16, so about 16GB of weights. Quantized to INT8 (1 byte per parameter) that drops to about 8GB, and to INT4 (0.5 bytes) about 4GB. For a deeper walkthrough of the VRAM math across model sizes and quantization formats, see the VRAM tier guide for self-hosting LLMs.

An 8B model at FP16 leaves the T4 with nothing to work with. The weights alone consume the whole card once you account for CUDA context and driver overhead, so there's no room for KV cache, meaning no room for context length or concurrency. The A10G at the same precision still has 8GB free, enough for moderate concurrency at typical chat context lengths. This is the real reason A10G costs more per hour: it's the difference between "runs an 8B model unquantized" and "doesn't."

Where T4's 16GB and Missing FP8 Support Become the Ceiling

Quantization is what actually makes the T4 usable for 7B-8B serving. Cut the model to INT4 with AWQ or GPTQ and it drops to roughly 4GB, leaving 12GB of the T4's 16GB for KV cache, which supports real concurrency. The AWQ quantization guide walks through the full process, including the vLLM deployment step; if you're setting that up on rented hardware, Spheron's vLLM server guide covers getting an OpenAI-compatible endpoint running.

The FP8 point matters here too, but not in the direction most people assume. Neither card has FP8 tensor cores, so INT8 and INT4 are the lowest-precision paths available on both, not just the T4. The ceiling isn't "T4 lacks a feature A10G has." It's that both cards are stuck one generation behind FP8, and the T4's 16GB just runs out of room for that tradeoff sooner than the A10G's 24GB does.

Cost Per GPU-Hour: A10G vs T4 Across Cloud Providers

The T4 is cheaper on every provider that lists both, but the size of the gap and the way it's billed vary a lot, and one provider's pricing is actively misleading if you don't read the SKU closely.

Multi-Provider Pricing Table

Sources: Vantage AWS g4dn.xlarge and g5.xlarge, Economize Cloud Azure NC4as_T4_v3, Vantage Azure NV6ads_A10_v5 and NV36ads_A10_v5, Economize Cloud GCP n1-standard-4 and GCP GPU pricing comparison, RunPod pricing, Vast.ai T4 and A10 marketplace pages, and the Hugging Face Inference Endpoints alternatives guide for HF's published dedicated-tier pricing.

Pricing fluctuates based on GPU availability. The prices above are based on 31 Jul 2026 and may have changed. Check current GPU pricing → for live rates.

Spheron's own catalog doesn't carry A10G, T4, or L4 right now. The entry point on the platform is L40S and up, as mentioned earlier. If you're pricing this tier specifically, the table above is the comparison; if you decide you want more headroom than either card gives you, that's a separate decision covered later in this post.

The Azure Fractional-GPU Trap

Azure's A10 access through the NVadsA10v5 series is fractional by default, and the pricing makes that easy to miss. The cheapest SKU, NV6ads_A10_v5, runs $0.454/hr and provisions only 1/6 of an A10, about 4GB of usable VRAM, according to Microsoft's own NVadsA10v5 series documentation and Vantage's pricing page for that SKU. A full 24GB A10 only shows up at NV36ads_A10_v5, which runs $3.200/hr, roughly 3.2x AWS's $1.006/hr for the same 24GB of GPU memory, per Vantage's NV36ads_A10_v5 page.

That $3.2/hr isn't Azure gouging on the GPU. It's the SKU bundling 36 vCPUs and 440GB of system RAM you don't need for a single-GPU inference box. But if you're comparing "Azure A10 pricing" against AWS's g5.xlarge headline number without checking which SKU actually gives you a full 24GB card, you'll either provision 4GB by accident or pay 3x more than you expected. Read the accelerator-memory column before the price column on this one.

Marketplace Pricing (Vast.ai) vs Hyperscaler Pricing

Vast.ai's peer-to-peer marketplace lists T4 from $0.13/hr and A10 from $0.20/hr, both well under 25% of AWS's on-demand rates for the same hardware. That's not a typo or a stripped-down SKU. It's the standard marketplace discount that comes from unmanaged, interruptible community capacity instead of a hyperscaler's managed fleet. The tradeoff is reliability: marketplace hosts can go offline without the SLA guarantees a hyperscaler or a managed neocloud provides, so it's a fit for batch jobs and experimentation, not a customer-facing endpoint that needs to stay up.

Throughput Per Dollar for High-Volume, Low-Complexity Inference

Raw hourly price only tells half the story once a GPU is actually serving traffic instead of sitting idle. Using AWS's on-demand rates as the cleanest apples-to-apples comparison (both are dedicated, non-fractional instances), the A10G costs 1.9x more than the T4 per hour ($1.006 vs $0.526). But NVIDIA's datasheet-reported throughput gap is bigger than that:

Divide the throughput ratio by the price ratio and the A10G comes out ahead on cost-per-unit-of-work for both workloads, not just cost-per-hour. That's the opposite of what the raw hourly prices suggest at a glance. The same logic underpins the cross-model, cross-GPU cost-per-token math in the GPU cost-per-token benchmark guide, which runs this comparison at the token level for several open-source LLMs.

The catch: this math only pays off if you're actually saturating the GPU. If your traffic is bursty and the card spends most of its time near-idle, you're paying for uptime, not throughput, and the T4's lower hourly floor wins regardless of what it can do at batch 128. High-volume, steady-state serving is where the A10G's price premium earns itself back. Low-volume, bursty serving is where the T4's lower price wins by default.

When to Skip Both and Go Straight to an L4

If you're not locked into either card by an existing deployment, it's worth asking whether you should be shopping this tier at all. RunPod already made that call for its entire catalog: it dropped A10G, A10, and T4 in favor of L4 and newer, according to its current pricing page. The L4 is Ada Lovelace, has native FP8 tensor cores that neither the T4 nor A10G supports, and lists at $0.39/hr on RunPod, cheaper than AWS's T4 rate and less than half AWS's A10G rate. The L4 vs L40S comparison has the full spec breakdown if that tier fits your workload better.

The A10G still has one real advantage the L4 doesn't: double the memory bandwidth (600GB/s vs the L4's 300GB/s) at the same 24GB VRAM capacity. For bandwidth-bound decode workloads at higher batch sizes, that matters more than FP8 support does. So this isn't a strict "always pick L4" conclusion. It's a tradeoff between an older card with more bandwidth and a newer one with FP8 and lower power draw. For a broader view of where all of these sit against H100-class hardware, the best GPU for AI inference guide and the best NVIDIA GPUs for LLMs guide cover the tiers above this one.

Decision Matrix: A10G vs T4 vs L4 by Workload

If you land in the A10G or L4 column above and want to test real throughput on your own model before committing to a monthly spend, Spheron's cheapest current-generation card is the RTX 4090, on-demand with no long-term contract.

RTX 4090 on Spheron →