Do these 3 things before closing this tab:
1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesiTechGuides is reader-supported. When you buy through links on our site, we may earn an affiliate commission. As an Amazon Associate I earn from qualifying purchases. Learn more
To choose an open model for a single GPU, estimate whether the complete inference workload fits in that GPU’s available VRAM—not just whether the model’s parameter count sounds small enough. Memory use depends on the model’s weight format and precision, the KV cache needed for your context length and simultaneous requests, and the serving runtime. A reliable choice is the exact model, format, and runtime tested on the GPU you plan to use.
Why parameter count does not tell you whether a model fits
Parameter count is a useful description of model size, but it is not a VRAM requirement. The weights are only one part of inference memory. Their footprint depends on how they are represented, while the runtime also needs memory for the KV cache and its own operations.
The vLLM authors’ 2023 deployment table makes the distinction visible. For its 13B configuration, it reports 26 GB for parameter memory and 12 GB for KV-cache memory on one A100 with 40 GB of total GPU memory. Those figures describe that paper’s configuration—not a universal requirement for every 13B model, precision, context, or inference engine. Read the vLLM paper.
The Tool Desk
Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →The same table reports 132 GB of parameter memory and 21 GB of KV-cache memory for its 66B configuration across four A100 GPUs with 160 GB total, and 346 GB of parameter memory plus 264 GB of KV-cache memory for its 175B configuration across eight A100-80GB GPUs with 640 GB total. These historical examples illustrate that cache demand can be substantial and that the reported deployments use multiple GPUs; they are not current sizing rules for other setups.
#1 Best Overall
- Axial-tech fans now feature a smaller fan hub that facilitates longer blades and a barrier ring that increases downward air pressure
- 2.5-slot design allows for greater build compatibility while maintaining cooling performance
- 0dB technology lets you enjoy light gaming in relative silence
- Dual BIOS switch lets you toggle between Quiet and Performance BIOS profiles
- Dual ball fan bearings last up to twice as long as sleeve bearing designs
What consumes VRAM during inference
Model weights and their precision
The published parameter count does not specify the weight format. Precision and quantization affect how much memory the weights occupy. A candidate listed with a given parameter count may therefore have different memory needs depending on the actual model file and its supported representation.
Weight quantization and KV-cache quantization are separate choices. Reducing one does not mean the other has also been reduced, and lower memory demand does not guarantee the same speed or behavior across models, GPUs, and runtimes. Check the specific format and engine rather than estimating from parameter count alone. vLLM’s quantization documentation lists supported formats, but support depends on the vLLM version and hardware.
Rank #2
- Powered by the NVIDIA Blackwell architecture and DLSS 4
- Powered by GeForce RTX 5070 Ti
- Integrated with 16GB GDDR7 256bit memory interface
- PCIe 5.0
- WINDFORCE cooling system
KV cache: context length and active requests
The KV cache stores information used during generation. Its demand changes with the context and the number of active sequences, so a model that fits for a short prompt and one request may not fit the workload you actually need.
Quick wins for a faster PC:
Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →In vLLM, insufficient KV-cache space can constrain serving. Its documentation describes reducing the number of sequences or batched tokens as ways to address cache pressure. See vLLM’s optimization and tuning guidance for details on cache allocation and configuration.
Rank #3
- Powered by the NVIDIA Blackwell architecture and DLSS 4
- Powered by GeForce RTX 5060
- Integrated with 8GB GDDR7 128bit memory interface
- PCIe 5.0
- WINDFORCE cooling system
Runtime allocation and available headroom
The serving engine also affects usable capacity. vLLM’s gpu_memory_utilization setting controls the fraction of GPU memory it uses, including memory allocated for the KV cache. Actual room for inference can be lower if another application is using the card or if the selected runtime reserves memory for its work.
For models that do not fit on one GPU, vLLM documents tensor parallelism as a deployment strategy. Its stable documentation says, “For models that are too large to fit on a single GPU (like 70B parameter models), tensor parallelism is essential.” That is guidance about using vLLM’s parallel-deployment approach, not proof that every model described by that parameter count exceeds every single GPU’s capacity. Representation, workload, and available VRAM matter. Read the vLLM optimization and tuning documentation.
Rank #4
- Powered by Radeon RX 9070 XT
- WINDFORCE Cooling System
- Hawk Fan
- Server-grade Thermal Conductive Gel
- RGB Lighting
How to compare open models for your GPU
- Identify the GPU and usable VRAM. Check the card’s VRAM and account for memory already occupied by other applications. Use the capacity available to your inference workload, not simply the card’s advertised total.
- Record each candidate’s actual weight format. Note the model file, precision, or quantization you intend to run. Do not infer the weight footprint from parameter count alone.
- Specify the workload. Decide the context length and number of simultaneous requests you need. These determine whether KV-cache demand is compatible with the memory left after weights and runtime allocation.
- Check engine compatibility. Confirm that the exact model architecture, quantization format, and GPU are supported by the inference engine and version you plan to use. A format listed as supported is not automatically supported on every hardware and software combination.
- Verify memory fit with the intended settings. If using vLLM, inspect its startup memory profile and cache allocation for your chosen version and configuration. A successful load alone is not proof that the target context and concurrency will work.
- Measure the workload that matters. Compare memory headroom, context and concurrency, task quality, and measured latency or throughput on the actual GPU and engine. Parameter count cannot rank these outcomes, and there is no universal cutoff supported without those details.
What to change if the workload does not fit
- Reduce concurrency or batched tokens if the immediate issue is KV-cache pressure and fewer simultaneous requests are acceptable.
- Consider a different weight or cache precision if the engine supports it for your hardware and model. Treat the two quantization choices separately, and check performance and behavior rather than assuming a memory saving is free.
- Choose a smaller model if you need the requested context and concurrency but cannot make the current configuration fit.
- Consider more VRAM or a multi-GPU setup only if the target model and workload justify it. Size hardware for the complete workload, not a generic parameter-count rule.
Which option is best depends on the quality your task requires and the serving limits you can accept. The available evidence does not compare quality across candidate models or establish a best model for an unspecified GPU and workload.
PC Slower Than It Used to Be?
A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Crashes, No Sound, or Screen Glitches?
Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteQuick Recap
Best Value
- Axial-tech fans now feature a smaller fan hub that facilitates longer blades and a barrier ring that increases downward air pressure
- Phase-change GPU thermal pad helps ensure optimal heat transfer, lowering GPU temperatures for enhanced performance and reliability
- 2.5-slot design allows for greater build compatibility while maintaining cooling performance
- Dual-ball fan bearings last up to twice as long as standard conventional sleeve bearings designs
- 0dB technology lets you enjoy light gaming in relative silence
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
