With the announcement of NVIDIA’s Blackwell architecture, many local LLM enthusiasts are anticipating a wave of server-grade GPU sell-offs. The dream? A repeat of the P40 era – where affordable, high-VRAM GPUs flooded the market, making local AI inference setups more accessible than ever. But what’s the current reality, and is there a true successor to the P40 for running large-scale local LLMs?
In this analysis, we’ll explore the contenders for the next value king in the local LLM GPU space and examine what might drive the next wave of high-VRAM, cost-effective hardware suitable for running 70B+ mixture-of-expert (MoE) and reasoning models.
What Made the P40 a Great Choice for Local LLM Inference?
The NVIDIA Tesla P40 has remained one of the most budget-friendly 24GB VRAM GPUs available. At its lowest point, it was selling for as little as $100, though today, prices have risen to the $350–$450 range due to increased demand.
The NVIDIA Tesla P40 gained popularity among local LLM enthusiasts primarily due to its high VRAM capacity, affordability, and enterprise-grade reliability. With 24GB of GDDR5 memory, it provided enough headroom to run larger LLMs, making it a practical choice for those looking to experiment with high-parameter models. Compared to consumer-grade alternatives like the RTX 3090, the P40 was significantly more affordable, at times selling for a fraction of the price while still delivering sufficient performance for quantized inference. While not the fastest GPU available, its data-center-grade durability ensured long-term stability, making it a compelling option for DIY AI setups despite the need for additional cooling modifications.
However, it wasn’t a plug-and-play solution. The P40 is a passively cooled card, meaning users had to provide additional cooling, typically through custom setups with server chassis fans or aftermarket cooling solutions.
Now, with AI models growing larger and more demanding, the search for the next P40 has begun. Let’s look at the most promising contenders in the secondhand server GPU market.
The Contenders for the Next Local LLM Value King
As of March 2025, the GPU market remains somewhat distorted, with the lack of RTX 5090 and 4090 availability pushing the RTX 3090 to the $1,000 mark. Prices for server-grade GPUs fluctuate, but we expect more availability and price adjustments as cloud providers phase out older hardware. Here’s what’s currently in play:
NVIDIA Tesla V100 (Volta)
Specs: 16GB & 32GB HBM2, 900GB/s bandwidth
The NVIDIA Tesla V100 stands out as the most likely successor to the P40 in terms of affordability and usability, though it is unlikely to reach the ultra-low pricing that made the P40 so popular. However, it is becoming increasingly available on the secondary market, with PCIe V100 (32GB) models currently selling for around $1,300, while the 16GB version hovers around $700. If the 32GB variant drops below the $1,000 mark, it will emerge as one of the best-value options for running 70B (4-bit quantized) LLMs, providing both VRAM and extended context lengths. The key factor in determining its future affordability will be whether cloud providers begin decommissioning these cards in bulk. Meanwhile, the
NVIDIA Tesla A10 (Ampere)
NVIDIA A10 (Ampere) offers another alternative, featuring 24GB of GDDR6 memory and a bandwidth of 600GB/s, though its higher price point makes it less attractive strictly for inference workloads.
A strong alternative, but it’s currently selling for around $2,000 – making it hard to justify strictly for inference, especially since its memory bandwidth is lower than the V100.
NVIDIA A16 (Ampere)
Specs: 64GB VRAM (split into 4x 16GB GPUs), 200GB/s per GPU
At first glance, the A16 seems appealing due to its massive 64GB VRAM, but there’s a catch: the GPU is divided into four separate 16GB dies, each with only 200GB/s bandwidth. This significantly limits its usefulness for LLM inference. Currently priced around $2,000, it doesn’t make much sense because we now have cheaper systems like Ryzen AI MAX+ 395 that have faster bandwidth and more VRAM for less money.
NVIDIA A30 (Ampere)
Specs: 24GB HBM2, 933GB/s bandwidth
The A30 is essentially a cut-down version of the A100, using the same architecture but with lower power consumption. The problem? It’s rarely available on the secondary market, making it an unreliable option for budget-conscious LLM users.
NVIDIA A40 (Ampere)
Specs: 48GB GDDR6, 696GB/s bandwidth
For those with a larger budget, the NVIDIA A40 presents an excellent balance of VRAM capacity, power efficiency, and overall performance for local LLM inference. With 48GB of VRAM, it offers headroom for running large-scale models without the memory constraints seen in lower-capacity cards. Despite its 300W TDP, the A40 remains practical for workstation and server setups, thanks to its dual-slot design and standard 8-pin power connector, making integration relatively straightforward. While its current pricing remains high, once the market adjusts and prices drop, the A40 could become one of the most compelling options for enthusiasts looking to maximize inference performance without venturing into the ultra-premium tier of AI accelerators.
Currently, refurbished models are selling for $6,000, but this should decrease significantly once data centers begin upgrading to Blackwell-based hardware.
NVIDIA A100 (Ampere)
Specs: 40GB & 80GB HBM2, 1,555GB/s bandwidth
The NVIDIA A100 remains one of the most powerful AI inference GPUs available, but its pricing keeps it well out of reach for most local LLM enthusiasts. 40GB models from Chinese sellers on eBay are currently listed around $4,600, while the 80GB variants command even higher prices, making them impractical for most DIY setups. Although prices are expected to decline over time as data centers upgrade to newer hardware, the A100 is unlikely to become a mainstream choice for local inference, as more cost-effective alternatives with sufficient VRAM and bandwidth are already available in the secondary market.
Conclusion: What’s Next?
The P40 set a high standard for affordability in local LLM setups, but finding a direct successor at the same price point is unlikely. Instead, the market appears to be shifting toward a tiered approach based on budget and performance needs. In the near term, the V100 32GB stands out as the best value pick, particularly if prices drop below $1,000, making it a viable choice for running larger models with extended context lengths. For those seeking the best balance of performance and cost, the A40 emerges as a strong contender – provided that prices fall significantly as data centers transition to next-generation hardware. At the high end, the A100 remains the ultimate option for enthusiasts with a larger budget, offering unmatched bandwidth and memory capacity, though its pricing keeps it out of reach for most DIY AI setups.
We’re keeping a close watch on the market and will update when new opportunities arise. Until then, local LLM enthusiasts should stay patient, as the next wave of server GPU sell-offs could bring better deals in the near future.