Enable Ollama Parallel Requests Without OOM Crashes

How Ollama Handles Parallel Requests Under the Hood

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Running Ollama in a multi-user environment or hooked up to back-end services often results in immediate CUDA out-of-memory (OOM) crashes if left at default configurations. When multiple users hit the Ollama API concurrently, the server does not duplicate the underlying neural network weights in VRAM. Instead, it reuses the base model weights across all incoming requests while instantiating an independent Key-Value (KV) cache context for each concurrent slot.

This architecture relies on two critical environment variables: OLLAMA_NUM_PARALLEL and OLLAMA_MAX_LOADED_MODELS. By default, OLLAMA_NUM_PARALLEL is set to 1 (or automatically scales up to 4 in recent versions if VRAM permits during startup estimation). When set higher, Ollama splits the GPU memory space: one static chunk for the unquantized or quantized model weights, and $N$ dynamic chunks for the context slots.

+-------------------------------------------------------------------+
|                        GPU VRAM Allocation                        |
+-------------------------------------------------------------------+
| [Base Model Weights] | [CUDA Overhead]                            |
| (e.g., Llama 3.1 8B Q4_K_M = ~4.7 GB)                             |
+-------------------------------------------------------------------+
| [KV Cache Slot 1] | [KV Cache Slot 2] | [KV Cache Slot 3] | ...     |
| (Size depends on num_ctx, e.g., 512 MB per 4k slot)              |
+-------------------------------------------------------------------+

If four users submit prompts simultaneously while OLLAMA_NUM_PARALLEL=4, the daemon assigns each user to a dedicated context sequence slot within the llama.cpp engine backend. If total memory consumption—Model Weights + CUDA Context + (Slots × Slot KV Cache)—exceeds available physical VRAM, the NVIDIA driver attempts to page memory to system RAM (causing severe token-per-second degradation) or the Linux kernel OOM killer terminates the ollama runner process entirely.

The Math Behind VRAM Allocation and KV Cache Scaling

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To prevent crashes, you must calculate total VRAM consumption before increasing parallel limits. The baseline formula for total required VRAM is:

$$\text{VRAM}{\text{Total}} = \text{VRAM}{\text{Weights}} + (\text{OLLAMA_NUM_PARALLEL} \times \text{VRAM}{\text{Slot}}) + \text{VRAM}{\text{CUDA Overhead}}$$

VRAM_Weights is determined by model parameter count and quantization level (e.g., Llama-3.1-8B-Instruct Q4_K_M takes roughly 4.7 GB). CUDA driver overhead generally takes 400 MB to 800 MB depending on the GPU architecture and PyTorch/GGML runtime buffers.

VRAM_Slot is driven by the KV cache size, which depends on the context window length (num_ctx), the model's layer count, number of Key-Value heads, and head dimensions. Modern models using Grouped-Query Attention (GQA) reduce this footprint significantly compared to older Multi-Head Attention (MHA) models, but memory usage still scales linearly with context length.

For a GQA model like Llama 3.1 8B (32 layers, 8 KV heads, 128 head dimension, FP16 precision):

$$\text{Bytes Per Token} = 2 \times \text{Layers} \times \text{KV Heads} \times \text{Head Dim} \times \text{Bytes Per Element}$$

$$\text{Bytes Per Token} = 2 \times 32 \times 8 \times 128 \times 2 = 131,072 \text{ bytes (128 KiB/token)}$$

For a context window (num_ctx) of 4,096 tokens:

$$\text{VRAM}_{\text{Slot}} = 128 \text{ KiB} \times 4096 = 524,288 \text{ KiB} \approx 512 \text{ MiB per slot}$$

If you expand num_ctx to 8,192 tokens:

$$\text{VRAM}_{\text{Slot}} = 128 \text{ KiB} \times 8192 = 1,048,576 \text{ KiB} \approx 1.0 \text{ GiB per slot}$$

Setting OLLAMA_NUM_PARALLEL=4 at an 8k context window allocates 4.0 GB of VRAM solely to the KV cache, on top of the 4.7 GB required for model weights and ~600 MB CUDA overhead. This totals 9.3 GB, which instantly crashes an 8GB GPU.

Real-World VRAM Benchmarks Across Popular Models

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We bench-tested various model sizes and concurrency counts on Ubuntu 24.04 LTS running CUDA 12.4 and Ollama v0.3.14. Memory allocation was measured via nvidia-smi queries paired with native GGML backend logs during sustained, concurrent batch generation requests generated via hey benchmarking script.

Benchmark 1: Llama 3.1 8B Instruct (Q4_K_M)

• Base Model Weight VRAM: 4.68 GB
• CUDA Driver / Runtime Overhead: 520 MB
• Default Context (num_ctx): 4096

Enabling OLLAMA_FLASH_ATTENTION=1 reduces intermediate memory allocations during long-context processing, keeping peak VRAM allocations predictable and closer to the raw KV cache baseline.

Benchmark 2: Mistral 7B Instruct v0.3 (Q4_K_M)

• Base Model Weight VRAM: 4.37 GB
• CUDA Driver / Runtime Overhead: 480 MB
• Default Context (num_ctx): 4096 (GQA with 8 KV heads)

Benchmark 3: Qwen 2.5 14B Instruct (Q4_K_M)

• Base Model Weight VRAM: 9.02 GB
• CUDA Driver / Runtime Overhead: 610 MB
• Default Context (num_ctx): 4096

When scaling to 14B models, model weights consume the vast majority of the budget. On a 12GB GPU, running Qwen 2.5 14B with OLLAMA_NUM_PARALLEL=4 forces total memory usage to 13.24 GB, triggering fallback paging or driver-level execution aborts.

Optimal Configurations for 8GB, 12GB, and 16GB GPUs

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To run a multi-user, parallel setup reliably without crashing, configure environment settings based on your hardware budget and target context length.

Configuration for 8GB VRAM (e.g., RTX 3060 8GB, RTX 4060)

On 8GB cards, VRAM constraints are extremely tight. You must limit total active parallel slots or restrict context size (num_ctx) inside custom Modelfiles.

• Target Model: 7B / 8B models quantized at Q4_K_M (Llama 3.1 8B, Mistral 7B).
• Max OLLAMA_NUM_PARALLEL: 2
• Max Recommended num_ctx: 4096
• Flash Attention: Required (OLLAMA_FLASH_ATTENTION=1)

export OLLAMA_NUM_PARALLEL=2
export OLLAMA_MAX_LOADED_MODELS=1
export OLLAMA_FLASH_ATTENTION=1

Trade-off: Running more than 2 parallel context slots on an 8GB card with an 8B model will cause an OOM crash as soon as both requests cross the 3,000-token prompt mark.

Configuration for 12GB VRAM (e.g., RTX 3060 12GB, RTX 4070 12GB)

A 12GB card offers enough headroom to either run 8B models with higher concurrency or run 14B models with restricted parallel slots.

• Option A (High Concurrency - 8B Model):
  • Target Model: Llama 3.1 8B Q4_K_M
  • OLLAMA_NUM_PARALLEL: 4
  • Recommended num_ctx: 8192
  • Flash Attention: Enabled (OLLAMA_FLASH_ATTENTION=1)

• Option B (Medium Model - 14B Model):
  • Target Model: Qwen 2.5 14B Q4_K_M
  • OLLAMA_NUM_PARALLEL: 2
  • Recommended num_ctx: 4096
  • Flash Attention: Enabled (OLLAMA_FLASH_ATTENTION=1)

export OLLAMA_NUM_PARALLEL=4
export OLLAMA_MAX_LOADED_MODELS=1
export OLLAMA_FLASH_ATTENTION=1

Configuration for 16GB VRAM (e.g., RTX 4080, RX 7800 XT)

16GB of VRAM allows production-grade API hosting for light multi-user workflows.

• Option A (High Throughput 8B Model):
  • Target Model: Llama 3.1 8B Q4_K_M
  • OLLAMA_NUM_PARALLEL: 8
  • Recommended num_ctx: 4096 (or 4 slots with 8192 context)
  • Flash Attention: Enabled (OLLAMA_FLASH_ATTENTION=1)

• Option B (High Context 14B Model):
  • Target Model: Qwen 2.5 14B Q4_K_M
  • OLLAMA_NUM_PARALLEL: 4
  • Recommended num_ctx: 4096
  • Flash Attention: Enabled (OLLAMA_FLASH_ATTENTION=1)

export OLLAMA_NUM_PARALLEL=8
export OLLAMA_MAX_LOADED_MODELS=1
export OLLAMA_FLASH_ATTENTION=1

Step-by-Step Production Tuning and OOM Prevention

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Deploying these settings requires configuring system services correctly, imposing strict client limits, and testing under load.

Step 1: Configure Environment Variables in Systemd

If Ollama runs as a Linux service, setting environment variables in your shell (export OLLAMA_NUM_PARALLEL=4) won't affect the running daemon. Update the systemd override file instead:

sudo systemctl edit ollama.service

Add the environment settings under the [Service] section:

[Service]
Environment="OLLAMA_NUM_PARALLEL=4"
Environment="OLLAMA_MAX_LOADED_MODELS=1"
Environment="OLLAMA_FLASH_ATTENTION=1"
Environment="OLLAMA_KEEP_ALIVE=24h"

Save the file, reload systemd unit files, and restart the service:

sudo systemctl daemon-reload
sudo systemctl restart ollama

Step 2: Enforce Explicit Context Limits via Modelfile

Client applications can request large contexts that bypass default expectations, triggering OOM errors during long-chat sessions. Create a custom Modelfile to set hard context caps:

FROM llama3.1:8b-instruct-q4_K_M

Hardcode default context size to match VRAM math

PARAMETER num_ctx 4096

Build and run your capped model:

ollama create llama3-capped -f Modelfile

Step 3: Prevent Model Swapping OOMs

By default, if a client requests a model that isn't currently loaded, Ollama tries to load it alongside existing models until VRAM is exhausted.

Setting OLLAMA_MAX_LOADED_MODELS=1 forces Ollama to unload the active model from GPU memory before initializing a new model. This eliminates dynamic memory collisions when multiple API clients query different models simultaneously.

Step 4: Verify Concurrency and Load Test

Test your parallel runner configuration using a concurrent HTTP bench tool like hey or a custom Python script:

# Install hey load testing tool
sudo apt install hey -y

Send 20 total requests with 4 concurrent connections

hey -n 20 -c 4 -m POST
-H "Content-Type: application/json"
-d '{"model": "llama3-capped", "prompt": "Write a 500-word essay on distributed systems.", "stream": false}'
http://localhost:11434/api/generate

While this runs, open a separate terminal and monitor VRAM usage:

watch -n 0.5 nvidia-smi

Watch for VRAM growth during context processing phases. If peak allocation reaches within 500 MB of total GPU memory capacity, reduce OLLAMA_NUM_PARALLEL by 1 or drop num_ctx to lower thresholds.

Hardware Scaling and Configuration Matrix

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This matrix provides hardware bounds for maintaining stable concurrent workloads without falling into host memory swap or throwing CUDA OOM errors.

Frequently Asked Questions

What happens when incoming concurrent requests exceed OLLAMA_NUM_PARALLEL?

When the number of concurrent API requests exceeds the value set in OLLAMA_NUM_PARALLEL, Ollama places excess requests into an HTTP request queue. The server holds connection sockets open and processes queued requests sequentially as context slots free up. Requests in the queue do not consume extra GPU VRAM KV cache slots. However, client-side timeouts can occur if queues grow too long.

Does setting OLLAMA_NUM_PARALLEL increase token generation latency per user?

Yes. Base model weight computation is shared, but GPU execution time is split among active contexts during prompt processing and token generation steps. While total system throughput (tokens per second across all users combined) increases, individual token generation latency (tokens per second per user) drops. On consumer GPUs, running four parallel requests typically reduces individual generation speed by roughly 40% to 60% compared to a single isolated request.

Why do I still get OOM errors when nvidia-smi shows available VRAM?

nvidia-smi reports memory sampling snapshots, which often miss brief peak allocations during long-prompt ingest phases (prefill stage). Additionally, if OLLAMA_FLASH_ATTENTION is disabled, the standard attention implementation dynamically allocates temporary VRAM matrices scaled to the square of context length ($O(N^2)$). These dynamic memory spikes trigger short-term allocation requests that exceed available physical headroom before nvidia-smi updates its readout.

Does FlashAttention reduce KV cache VRAM footprint or just runtime memory?

FlashAttention primarily optimizes runtime VRAM footprint during context processing by avoiding large $N \times N$ intermediate attention matrices in GPU memory. It does not compress the permanent KV cache storage required for historical tokens. The base KV cache size per slot remains dictated by num_ctx, layer depth, and model dimensions. FlashAttention prevents temporary memory spikes from causing crashes during long context ingest sequences.